Culture and Market Integration: Evidence on
Mechanisms ∗
Sarah Walker
October 2016
Abstract
Culture is an important determinant of economic exchange, but what are the mechanisms through which culture operates? This article offers a novel explanation, arguing
that beyond the role of information-sharing, cultural networks distort trade when a
dominant group holds market power in the production of certain goods. A simple
model predicts that in the presence of observable cultural differences between firms,
price discrimination arises, thereby distorting market efficiency. The empirical analysis examines the relationship between culture and market integration in 19th century
Austria. I argue that Catholic market power in agricultural production, as a result of
institutional discrimination against religious minorities, inhibited market integration.
The empirical analysis establishes that religious differences between provinces increase
price gaps, with no observable effect of ethno-linguistic identity. Moreover, the data
confirm a price discrimination mechanism and reject an information-sharing story. The
estimates are robust to the consideration of reductions in transport costs, showing that
railroads failed to promote market integration due to the fact that connections were
built between religiously similar places, thereby reinforcing existing trade barriers.
JEL Codes: F15, N73, Z10, Q15
University of New South Wales, UNSW Business School, School of Economics. [email protected].
Comments and suggestions are welcome. Please do not cite or distribute without permission. I gratefully
acknowledge comments from Jennifer Alix-Garcia, Pauline Grosjean, Gabriele Gratton, Laura Schechter,
Jeffrey Williamson, Daniel Phaneuf, Volker Radeloff, and Florian Ploeckl, as well as seminar participants at
the University of New South Wales and 2015 Natural Experiments in Economic History Workshop. I am
thankful for funding from the NASA Land Use and Land Cover Change program under grants NNX11AE93G
and NNX12AG74G, as well as to Jessica Clayton for translation assistance.
∗
1
Introduction
What is the role of culture in facilitating trade? What are the channels through which
culture operates? In a world that is rapidly integrating – and sometimes separating – across
national, ethnic, and religious boundaries, understanding the role of culture in this process
is imperative.
A developing research agenda explores these questions by examining the effect of cultural
proximity on trade flows and trust (Felbermayr and Toubal, 2010; Guiso et al., 2009), the
role of language in promoting trade (Egger and Lassmann, 2015; Melitz and Toubal, 2014;
Egger and Lassmann, 2012; Melitz, 2008), and the network effects of culture on trade (Rauch
and Trindade, 2002; Rauch, 1999; Greif et al., 1994; Greif, 1993, 1989). A common challenge
with these studies, however, is the counfoundedness of culture with economic, legal, and
political institutions, as well as the difficulty of distinguishing the information aspects of
culture from other components.1
This paper studies one of the most multi-ethnic and religiously diverse states in modern
history – the Austro-Hungarian Empire – in order to identify the effects of culture on market
integration and elucidate the mechanisms through which culture affects trade. Network trade
theory posits that cultural networks promote trade through their ability to enforce contracts
and overcome informational barriers (Rauch and Trindade, 2002; Rauch, 1999; Greif et al.,
1994; Greif, 1993, 1989). While informational barriers are a notable impediment to trade,
this paper presents and tests an alternative mechanism: market power held by a dominant
cultural group that distorts trade through price discrimination against cultural minorities.
I argue that culture determined market integration in 19th century Austria through
Roman Catholic networks that monopolized the means to agricultural production – namely,
land. As a result of prolonged institutional discrimination against non-Catholics with regard
to land tenure, education, state employment, and guild membership, Catholics were able
1
Egger and Lassmann (2015) are able to identify the impact of a common native language (CNL) rather
than spoken language using a spatial regression discontinuity across Swiss municipalities, which identifies
causal effects of the cultural component of language on trade.
2
to exert market power in agricultural trade. Consequently, agricultural markets integrated
according to religious networks through a mechanism of price discrimination by Catholics
against religious minorities. Ethno-linguistic identity, while politically salient at the time,
as it fueled sometimes violent nationalist movements, was less important due to the absence
of institutionalized economic disenfranchisement of ethnic minorities.2
Using 19th century statistical records from the Habsburg monarchy I compile data on
religious and ethnic composition, as well as prices on grains, beer, and wine, in order estimate the effect of religious and ethnic differences on market integration across 14 Austrian
provinces. The empirical analysis finds that religious networks facilitate market integration,
while there is no evidence of an effect of ethno-linguistic networks. A 1 standard deviation
increase in the religious Manhattan index (a measure of religious dissimilarity between two
provinces) is associated with a 1.1 to 1.4 standard deviation increase in relative prices for
wheat, rye, and oats. This effect operates through Roman Catholic networks, where increasing the share of the joint Catholic population relative to the total population of a given
province-pair by 1 percentage point is associated with a 0.17 to 0.21 percent reduction in
relative prices.
I motivate the argument that these effects operate through a price discrimination mechanism by sketching a simple game-theoretic model, adapted from Fafchamps (2003). I empirically verify a price discrimination mechanism by first showing that there is no evidence
of an information channel. The network trade literature posits that information barriers to
trade are stronger for differentiated goods, since homogenous goods do not require signals
about important characteristics, such as quality. Therefore, if the empirical effect of culture
on trade is stronger for differentiated goods, this suggests that culture operates through an
informational channel (Rauch and Trindade, 2002; Rauch, 1999). The analysis below exam2
This argument is inconsistent with the findings of Schulze and Wolf (2012), who examine the effect of
economic nationalism on market integration in grain prices throughout Austria-Hungary from 1870-1910
and find that ethno-linguistic similarity substantially decreases price dispersion between cities. The authors,
however, do not explicitly examine the effects of religion, nor do they control for the physical distance
between cities, which in my estimations nullify the effect of ethnicity. They do control for changes in rail
distance, which I show in the analysis below to be endogenous to religion.
3
ines market integration for a set of homogenous (wheat, rye, and oats) and differentiated
(beer and wine) goods. There is no evidence of an effect of religious or ethnic networks on
market integration in differentiated goods, thereby ruling out an information mechanism. In
verifying the price discrimination channel, I find that more religious diversity is associated
with higher grain prices in a given province, after controlling for factors that affect prices,
such as population density, agricultural potential and productivity, and access to physical
trade networks, such as the Danube river. Increasing the proportion of non-Catholics by 1
percentage point in a given province is associated with a 0.16 to 0.50 percent increase in
grain prices.
The estimates are robust to the consideration of reductions in transport costs – namely
the establishment of railroad connections between provinces throughout the 19th century,
which should theoretically promote market integration. To analyze this, I reconstruct the
Habsburg railroad network from 1832 to 1910 and measure the effect of rail connectivity on
relative prices. After including yearly fixed effects, which control for general time trends
in market integration, there is no evidence that railroads promote market integration. The
result is likely due to the fact that railroads were endogenously built between provinces that
were already integrated through religious networks. Increasing the religious dissimilarity
index by 1 standard deviation is associated with a 0.5 to 0.7 standard deviation reduction in
the probability that two provinces receive a rail link. The implications of these findings suggest that transport infrastructure projects may have unintended consequences by reinforcing
existing trade-diverting networks, thereby distorting efficiency rather than promoting it.
This research relates primarily to the literature on culture and trade (Egger and Lassmann, 2015; Melitz and Toubal, 2014; Egger and Lassmann, 2012; Schulze and Wolf, 2012;
Felbermayr and Toubal, 2010; Guiso et al., 2009; Melitz, 2008). As Egger and Lassmann
(2015) note, a common problem in estimating the effect of culture, specifically language, on
trade is the confoundedness of omitted factors, such as religion and economic, political, and
legal institutions. The analysis below controls for these omitted characteristics by examining
4
trade patterns between regions that fall under the same imperial institutions. In addition,
I am able to disentangle two aspects of culture – religion and ethno-linguistic identity – by
examining their effects separately.
With regard to the literature on networks and trade (Chaney, 2014; Rauch and Trindade,
2002; Rauch, 1999; Greif et al., 1994; Greif, 1993, 1989), which argues that a primary function
of networks is to enforce contracts and overcome informational barriers, this paper presents
and tests an alternative mechanism. A price discrimination channel offers an explanation
for why we may observe stronger effects of cultural networks on market integration across
homogenous goods, for which information is not a large barrier, rather than differentiated
goods. Although historical in nature, this research is currently relevant to culturally diverse
regional economies today, such as Sub-Saharan Africa, the Middle East, or Southeast Asia,
where certain ethnic or religious groups own the means to production, leaving distortionary
effects on market exchange. For instance, ethnic Chinese conglomerates often monopolize
certain industries in Southeast Asian countries, such as Singapore, Indonesia and Malaysia,
while agricultural value chains are largely distinguished along ethnic lines in Kenya. In the
context of African manufacturing, Fafchamps (2000) finds that access to supplier credit in
Kenya and Zimbabawe is largely determined by ethnicity and gender.
This work is also related to a growing literature examining the effects of transportation networks on market integration (Baum-Snow et al., 2015; Donaldson, 2014; Keller and
Shiue, 2015, 2008), which overwhelmingly finds that reductions in transport costs from improvements in transport infrastructure promote market integration in prices. The current
research finds no evidence of an effect of railroads on market integration due to the fact
that railroads were endogenously built between provinces that were religiously similar. This
finding highlights the difficulty in estimating causal effects of transportation networks, as
well as the extent to which large and expensive infrastructure projects can reinforce existing
trade-diverting barriers, rather than mitigate them.
Lastly, this research makes an important methodological contribution to the estimation
5
of trade data with dyadic observations. Cameron and Miller (2014) show that trade regressions with paired or dyadic data have a complicated pattern of error correlation that when
unaccounted for may lead to grossly under-estimated standard errors and result in overrejection using standard hypothesis tests. Cameron et al. (2011) propose a two-way robust
cluster methodology for standard errors that accounts for this pattern of error correlation,
but the method is widely unused in the trade literature. This paper is one of the first to
apply the two-way cluster method to trade data and therefore offers conservative estimates
of the effects of culture on market integration.
The paper is organized as follows: Section 2 provides historical background, with particular attention dedicated to the ethnic and religious context of 19th century Austria. Section 3
presents the theoretical framework that motivates the empirical strategy outlined in Section
4. The primary estimation results are presented in Section 5, with a further discussion of
important omitted factors, specifically railroads, in Section 6. Section 7 concludes.
2
Historical Background
As Figures 1 and 2 illustrate, the Habsburg monarchy spanned much of what is Central
and Eastern Europe today, with two major regions divided between Austrian and Hungarian
lands. Until the mid 19th century the empire functioned as a composite monarchy in which
the Kingdom of Hungary faced limited institutional privileges under the Austrian Empire.
The founding of the Dual Monarchy in 1867 established political autonomy for Hungary with
the creation of separate parliaments for Austria and Hungary, while economic and military
institutions remained unified under a single emperor.
The Habsburg monarchy was not a closed economy per se, as it enacted several trade
liberalization measures in the second half of the 19th century, most notably the tariff reductions of 1851, 1853, and 1865.3 Nonetheless, the overwhelming majority of 19th century
3
Many of these tariff reductions, however, were overturned following the 1873 recession. Restrictions on
trade within Austria and within Hungary were abolished in the late 18th century, while tariffs between the
6
Habsburg trade occurred within and between the regions of Austria and Hungary, rather
than internationally (Eddie, 1977). Data scarcity for Hungary limits the ability to examine
market integration throughout the entire monarchy. The analysis that follows is therefore
restricted to the 14 Austrian provinces, known as Cisleithania and illustrated in Figure 2,
for which data are available for most of the 19th century.
2.1
Ethnicity and Religion
The Habsburg monarchy was one the most multiethnic and religiously diverse states of
its time, with over ten different ethno-linguistic groups and all of the major European religions represented throughout the empire. Tables 1 and 2 display the ethnic and religious
composition of the 14 Austrian provinces. German was the dominant ethno-linguistic group
in Cisleithania, with German speakers comprising 100% of the population in some provinces.
Though, considerable variation existed across regions, with significant Czech, Polish, Ruthenian, Slovenian, and Serbian populations in certain provinces. Religious composition was far
more concentrated, with Roman Catholics representing an overwhelming religious majority.
Nonetheless, there were significant Greek Catholic, Orthodox, and Protestant populations
in some regions, as well as an established Jewish minority.
Habsburg economic historians often highlight the stark contradiction between the rise of
nationalist movements motivated by ethno-linguistic identity and the monarchy’s efforts at
inter-regional economic integration throughout the 19th century (Schulze and Wolf, 2012;
Bruckmüller and Sandgruber, 2003; Good, 1984). The revolutions of 1848-49 carried out by
nationalist groups throughout Austria, and most violently in Hungary, are commonly thought
to have initiated a period of liberal reforms in the following decades. The Dual Compromise
of 1867 was certainly an appeal to Hungarian economic nationalism, although nationalistic
sentiments among other ethno-linguistic groups endured, and in many cases intensified, until
the empire’s demise in 1918. Starting in the 1870s, industrial and agricultural credit cotwo regions remained until the formation of an Austro-Hungarian Customs Union in 1850 (Eddie, 1977).
7
operatives founded along ethno-linguistic lines played integral roles in national movements,
while demonstrations were frequently accompanied by the slogan, “everyone to his own kind”
(Bruckmüller and Sandgruber, 2003). Ethno-linguistic conflict is often cited as the catalyst
for the collapse of the monarchy, lest one forget the assassination of Archduke Franz Ferdiand
in 1914 by Serb and Bosnian nationalists, leading directly to the First World War.
While political tensions rooted in ethno-linguistic identity were a central feature of 19th
century Austria-Hungary, institutional discrimination against particular ethnic groups did
not exist.4 Religion, however, formed the basis for several legal and economic policies that
were deeply entrenched in Habsburg institutions since the monarchy’s inception. For centuries, only Roman Catholics had the right to own land, worship in public, attend public
schools and universities, or hold positions in state institutions. With the tolerance reforms
of Joseph II in 1781, Protestants, Eastern Orthodox Christians, Greek Catholics, and later
Jews, became officially “tolerated” under the law and were granted rights to join guilds, attend state universities, and serve in the military. Religious discrimination in land ownership,
however, endured until the reforms of the post-1848 revolutionary era (Judson, 2016).
Although institutionalized religious discrimination ended in the mid-19th century, the
monarchy made great efforts to increase the rights and influence of the Catholic church over
the same period. Mostly notably was the 1855 concordat with the Vatican, which established
the Catholic church’s control over clerical appointments, marriage, and education within the
monarchy. Other informal measures included the rise of “ultramontanism”, an aggressive
Catholic ideology that asserted the absolute primacy of the pope within the Church and
over secular institutions, as well as a surge in the construction and renovation of Catholic
churches, particularly in working-class neighborhoods (Judson, 2016).
Religion therefore played an institutional role in economic disenfranchisement throughout the monarchy in ways that ethno-linguistic identity did not, which inevitably affected
the structure of land tenure. Beginning in the late 17th century, Habsburg rulers made a
4
The main exception is people of Jewish identity, who for the purpose of this discussion are considered a
religious group.
8
concerted effort to decrease the presence of Protestant nobles by promoting the rise of a
new Catholic nobility. The number of Catholic admissions to the Estate of Knights more
than tripled between 1600 and 1609. Protestant admission to the Estate of Lords declined
by half between 1609 and 1619, such that by 1613 only one Protestant family was incorporated (MacHardy, 2003). Hence, by the turn of the 19th century, the landed nobility in
many regions of the monarchy were Catholic. Moreover, the post-1848 legislation introduced
“fidei-commissum” - the legally institutionalized entailed landed estate – which occupied 2.2
million hectares of land in the Austrian provinces, ensuring that land would remain in the
families of nobles. Consequently, a sizable portion of agricultural land was predominated
by large estates (properties in excess of 500ha) whose land owners served as an important
political force (Berend, 2003).5
Disaggregated data on landownership by religion of owner is unfortunately difficult to
acquire.6 In Figure 3, correlations at the provincial level reveal that places with a higher
proportion of non-Catholics had a more unequal distribution of land. To the extent that
land is a primary input into agricultural production, it is possible that historically exclusive
Catholic ownership of land distorted the development of agricultural markets throughout the
monarchy, as Catholic landed elites exerted market power over trade in agricultural goods.
There is additional evidence that religious discrimination also affected education outcomes in the 19th century, where primary school enrollment rates were consistently lower in
provinces with larger shares of non-Catholics than regions that were predominantly Catholic.
Table 5 suggests that increasing the share of the non-Catholic population by 1 percentage
point is associated with a 0.26 to 0.33 percentage point reduction in primary school enrollment.7 Such observable differences in educational attainment by religious status may have
5
Large noble estates occupied between 1/3 to 1/2 of the land throughout the monarchy. By the mid-1850s,
30% of land remained in the hands of 151 noble families. By the turn of 20th century, large estates comprised
29 percent of total land in Austria, while a few hundred families of the Hochadel (the high aristocracy) owned
at least 1/5 of the land (Berend, 2003).
6
Cadastral level data exists in historical archives in Vienna. No digitized data is available to the best of
the author’s knowledge.
7
Standardized beta coefficients suggest that a 1 standard deviation increase in the non-Catholic population is associated with a 0.37 to 0.41 standard deviation reduction in primary school enrollment. The
9
led to perceptions that non-Catholics were less competent than Catholics. This perception, combined with Catholic market power in agricultural production, is a potentially ripe
environment for price discrimination – a hypothesis that motivates the theoretical model,
below.
3
Theoretical Motivation
The empirical analysis is primarily concerned with understanding the mechanisms through
which culture, an intangible barrier to trade, affects market integration. Existing theory typically models trade barriers as “iceberg” costs (T ), where Tij ≥ 1 for goods produced in region
i and shipped to j, and Tii = 1 domestically.8 Trade costs drive a price wedge between regions, where pij = pii Tij , such that as barriers diminish and Tij → 1, prices converge. Taking
the log of both sides of the price identity yields an equation that is intuitive for empirical
estimation: ln(pij ) − ln(pii ) = ln(Tij ). Price gaps between regions are expressed in terms of
trade costs between the two, where costs can take several forms, including physical distance,
bilateral tariffs, transportation costs, or cultural dissimilarities.
Although elegant in its simplicity, the iceberg characterization of trade costs provides
little intuition for understanding the channels through which culture affects trade. Given
the discussion in Section 2, in which institutional discrimination against religious minorities
lead to unequal land ownership and lower education among non-Catholics, I argue that
culture determined market integration in 19th century Austria through Catholic market
power in agricultural production and price discrimination against religious minorities. As
theoretical motivation, I sketch a simplified version of a game-theoretic model of trust-based
exchange by Fafchamps (2003), which illustrates how observable differences in the population
results are somewhat interesting given the Protestant emphasis on literacy (Becker and Woessmann, 2009).
Alternative specifications that separate Protestants from other non-Catholics show a small and statistically
insignificant positive relationship between the proportion of Protestants and school enrollment, while the
relationship between non-Catholic / non-Protestant population shares remains negative and statistically
significant (results available upon request).
8
See Anderson (1979) and Anderson and van Wincoop (2003) with specific regard to gravity models and
border effects. See Dornbusch et al. (1977) and Eaton and Kortum (2002) for predictions on prices.
10
of firms, such as ethnicity, religion, or gender, can distort market integration through price
discrimination.
Consider an economy with a large number of firms that live indefinitely and discount the
future with a factor δ. Firms trade with each other in each period, and for simplicity, assume
that there is one supplier and one client in each exchange. Suppliers decide whether or not to
grant trade credit to their client, since payment in cash is risky and costly to administer. In
addition, there are two types of firms: competent firms who gain from trade, of proportion
1−µ, and incompetent firms who only profit by failing to repay trade credit, of proportion µ.
The proportion of competent and incompetent firms in the economy is constant and known,
but firm type is private information. In order to ensure repayment, firms screen potential
customers at a cost c that is sufficient to identify incompetent firms after one period. If
a client/supplier is deemed competent, trade commences in the next period. Before trade
commences, suppliers and clients are considered “unmatched”. Once a supplier and client
decide to trade, they are considered “matched” and continue trading in future periods.
The extensive form of the general game where observable cultural differences are not
accounted for is presented in Figure 4. The payoff to client C is first and the payoff to supplier
S is second. Payoffs are normalized such that the short-term profit of a competent firm who
does not receive trade credit is 0. The expected payoff of an unmatched supplier is V N and
the continuation payoff of a matched supplier is V S . The corresponding payoff for clients are
V U and V C . Note that in the case of matched firms, the net profit margins for suppliers and
clients are α and β, respectively. A client that does not repay has an instantaneous payoff of
1 > β, such that clients always have a short-term incentive to default. In this case, suppliers
make a loss of -1 and begin searching for a new client in the next period.
Fafchamps (2003) focuses on a set of trust-based strategies, meaning that there is no
external enforcement of trade credit repayment. Nature randomly pairs firms with competent
or incompetent firms with probabilities 1 − µ and µ, respectively. Incompetent clients always
cheat suppliers, such that unmatched suppliers screen new clients. In the event the client
11
is incompetent, suppliers screen another firm in the next period until a competent client
is identified. Suppliers then offer trade credit and the firms trade in the long-run. The
combined gains from trade are α + β = κ, over which suppliers and clients negotiate. Note
that a supplier can increase profit margins by raising the price (i.e., increasing α at the
expense of β).
The expected continuation payoffs for matched and unmatched firms under the trustbased strategies are thus:
V S = α + δV s
V N = µδV N + (1 − µ)δV S − c
V C = β + δV C
V U = µδV U + (1 − µ)δV C
In order for trust-based strategies to be sub-game perfect, the following equilibrium conditions must hold: it is profitable for suppliers and clients to continue trading (equations
(1) and (2) below); clients must have an incentive to repay suppliers (3); it is beneficial for
clients and suppliers to screen each other (equations (4) and (5)); and it is not rational to
provide instant trade credit (6). That is:
VS ≥VN
(1)
VC ≥VU
(2)
β + δV C ≥ 1 + δV U
(3)
VN ≥0
(4)
VU ≥0
(5)
V N ≥ µ(−1 + δV N ) + (1 − µ)(α + δV S )
(6)
Now consider an economy in which the firm population is comprised of two separate
groups (ethnic, religious, language, gender, etc.), A and B, whose characteristics are observ-
12
able. The proportion of A and B firms in the economy is θ and 1 − θ, respectively, while
the proportion of incompetent firms in each group is µA and µB . Let the proportion of
incompetent firms in group B be larger than group A, such that µB > µA .9 This could be
due to lower education in group B, a lack of commercial experience, or a weaker financial
base, as was true for religious minorities in 19th century Austria.
When cultural types are observable and when one group is less competent, markets may
fail to integrate under under price discrimination. To see this, assume that with probability
θ, suppliers are matched with A clients, who are screened and a proportion µA are deemed
competent. With probability 1 − θ, suppliers are matched with B clients, who are screened
and a proportion µB are deemed competent. Suppliers’ expected payoffs are thus:
V S = α + δV S
V N = δV N (θµA + (1 − θ)µB ) + δV S (θ(1 − µA ) + (1 − θ)(1 − µB )) − c
The conditions under which price discrimination may arise can be found by first computing the equilibrium values for V S and V N , and then by considering the screening condition:
µi δV N + (1 − µi )δV S ≥ V N , for i ∈ A, B. That is, it must be in suppliers’ best interest
to screen all clients. Solving the screening condition yields the minimum trade margin that
∗
∗
suppliers must receive in order not to screen A and B clients: αA
and αB
, respectively.
Comparing the two trade margins reveals the conditions under which price discrimination
may arise:10
∗
∗
αB
− αA
=
c(µB − µA )(1 − δθµA − δ(1 − θ)µB )
δ(1 − µA )(1 − µB )
(7)
Notice from equation (7) that the minimum trade margin suppliers require from B clients
9
Fafchamps (2003) shows that a price discrimination equilibrium may also arise in the absence of observable differences between the two groups. This occurs under network effects in which there is an ‘in’ and ‘out’
group. Price discrimination arises when members of the ‘in’ group share information exclusively with each
other and therefore trade more easily within their own network. Clients in the ‘out’ group may therefore face
discrimination in terms of prices, rationing, or exclusion. The empirical analysis is concerned with network
effects beyond information sharing and therefore does not present theoretical motivation for this mechanism.
10
Proofs for a more complicated version of this model in which trade patterns between matched suppliers
and clients can randomly change over time may be found in Fafchamps (2003).
13
increases relative to A clients as screening costs rise and the gap between the two populations
increases. Recall that since a buyer’s profit margin is β = κ − α, increasing α means
decreasing β, or equivalently, increasing the price charged to buyers. In this environment,
price discrimination occurs outside of the information sharing effects of networks.
In the context of 19th century Austria, it is hypothesized that price discrimination occurs
along religious lines as a result of ongoing institutional discrimination against religious minorities. The empirical strategy below first measures the extent to which culture determines
market integration by estimating the effect of religious and ethnic dissimilarities between
provinces on their relative prices. Next, in order to substantiate a price discrimination
mechanism, I examine whether regions with a larger concentration of religious minorities
faced higher prices across a variety of goods. I rule out an information-sharing mechanism
by showing that the effects of culture on market integration are stronger for homogeneous
goods, for which information is relatively unimportant, rather than differentiated goods, for
which information is required to signal characteristics such as quality (Rauch, 1999; Rauch
and Trindade, 2002).
4
Data and Estimation Strategy
4.1
4.1.1
Data
Price data
Prices were obtained from a set of annual statistical yearbooks (Austria, 1865, 1881, 1914)
for the 14 Austrian provinces for the years 1829-1910. The original price data reported in the
yearbooks were first collected by local authorities that operated at various trading locations
within regional towns. Local data were then aggregated and averaged to the provincial level
annually by the central statistical office (Cvcrek, 2013). Price data, therefore, reflect average
prices within a given province in a given year. Unfortunately, price data is not consistently
14
available for all commodities over the years. Data are most reliable for wheat, rye, and
oats – the most heavily traded agricultural commodities, which are relatively homogeneous
– and slightly less reliable for beer and wine, which are arguably more differentiated than
grains.11 Note that after 1893 several provinces inconsistently reported price data (Carinthia,
Carniola, Silesia, Dalmatia, and Moravia failed to report in most years). In order to limit
potential reporting bias, I therefore restrict most estimations to the years 1893 and earlier.
Figure 5 displays trends in relative prices for all Austrian province pairs using a smoothed
local polynomial regression of the relationship between relative prices and time. The relative
prices of wheat, rye, and oats declined steadily over the study period, suggesting a general
trend of market integration in agriculture over the course of the 19th century. Relative prices
for beer are slightly more stagnant, although there is a general downward trend over time.12
4.1.2
Cultural data
Data on the religious and ethnic composition of each province come from the Tafeln zur
Statistik (Austria, 1865). Religious composition is consistently available for the years 18311857, and intermittently available thereafter (1857, 1869, 1880) in later yearbooks (Austria,
1881, 1914). Data on ethnicity is available in the Tafeln zur Statistik for the years 1851 and
1857. I use the religious and ethnic composition data to construct measures of culture both
between and within provinces. In order to measure cultural differences between provinces, I
construct two separate Manhattan distance indices – one for religion and one for ethnicity –
using the following formula:
Religious or Ethnic dissimilarity =
X
|pi − qi |
(8)
i
11
Beer and wine prices are reported in the yearbooks as “high” and “low” for a given province in each
year. The prices used in the empirical analysis are the average of high and low prices in a each year.
12
One may be concerned that beer (or wine) was a non-traded good, which could explain the flatter slope.
Internationally, Austria-Hungary was significantly engaged in the beer trade, specifically with Germany. As
of 1893, Austria-Hungary was the second largest importer of German beer, after Switzerland, and the second
largest supplier of beer to Germany, after England (United States Congressional serial set, 1893). In regard
to wine, the only Habsburg region with any substantial production in wine was Dalmatia (Austria, 1865),
such that most regions were net importers of domestic, as well as international, wine.
15
where pi is the share of the population in province p belonging to religious or ethnic group
i, and qi is the share of the population in province q belonging to religious or ethnic group
i. Note that the index has a theoretical maximum of 2.
Within provinces, I construct measures of the proportion of the population that did not
belong to the dominant cultural groups in order to measure the extent to which cultural
minorities faced price discrimination. The main variables of interest are the proportion
of non-Catholics and non-Germans. As the theoretical framework suggests, increasing the
proportion of cultural minorities should increase the degree of price discrimination within
provinces.
Figure 6 and Tables 1-4 illustrate the various measures of culture. There is considerable
ethnic heterogeneity within provinces, with German being the largest ethno-linguistic group
in many places; although there is noticeable variation across provinces. Religious composition
is much more concentrated, with many provinces having over 97% Catholic population shares.
Compared to other regions in Central Europe at the time, the Habsburg monarchy was
slightly more concentrated in terms of religion. For instance, religious composition in the
German lands varied from 65% Protestant and 34% Catholic in Prussia to 97% Protestant
and 2% Catholic in Saxony to 28% Protestant and 71% Catholic in Bavaria as of 1871
(MacCaffrey, 1905).
4.1.3
Other data
As controls in the province-level regressions, I include data on wheat, rye, and oats
production per capita, as well as measures of population density and steam engine use as
a proxy for the major technological developments of the industrial revolution. These data
are taken from the annual statistical yearbooks (Austria, 1865, 1881, 1914). In addition, I
include a measurement of each province’s distance to an ancient Roman road in order to
control for the effect of being connected to historical trade routes. The Roman road network
data come from the Digital Atlas of Roman and Medieval Civilizations (McCormick et al.,
16
2013). Lastly, I include geo-physical data on ruggedness from Nunn and Puga (2012) and
soil quality from the European Soil Database, in order to control for factors that affect
agricultural production, and hence prices. Note that the measure of soil quality is equal
to one if the soil in a given area has “no agricultural limitations” (European Commission,
2004). In my dataset, these values are averaged at the province level. Summary statistics
for all data used in the empirical analysis are displayed in Table 6.
4.2
Estimation Strategy
The empirical strategy first establishes a relationship between culture and market integration, and then identifies the mechanisms through which culture affects trade. Therefore,
I begin by estimating the following equation across all province dyads:
|ln(pit ) − ln(pjt )| = α + βReligionij + γEthnicityij + φXij + νit + νjt + εijt
(9)
where, ln(pit ) is the natural log of prices in province i in year t, Religionij is the Manhattan
distance index of religious dissimilarity between provinces i and j in 1851, Ethnicityij is
the ethnic dissimilarity index between provinces i and j in 1851, Xij is a vector of dyadic
controls, including the natural log of the distance between the centroids of provinces i and
j and the average ruggedness and soil quality of the two provinces in the dyad, and νit and
νjt are a full set province by year fixed effects for each province in the dyad.13 Finally, εijt
is the error term, which is clustered using two-way cluster robust standard errors (Cameron
et al., 2011; Cameron and Miller, 2014).
Note that trade regressions typically cluster standard errors at the country-pair (or in
this case, province-pair) level. Cameron and Miller (2014) show, however, that regressions
with paired or dyadic data have a complicated pattern of error correlation that when unaccounted for may lead to grossly under-estimated standard errors and result in over-rejection
13
All dyad estimations include individual province i and province j fixed effects, separate year fixed effects,
as well as the interaction of province i x year and province j x year fixed effects.
17
using standard hypothesis tests (Cameron and Miller, 2014). The intuition behind two-way
clustering is based on the fact that model errors are likely to be correlated between dyads
that have an observation in common. Cameron and Miller (2014), give the example that,
for instance, errors for US-UK trade may be correlated with those for any other countrypair that includes either the US or UK. The authors prove that standard approaches, such
as including country fixed effects or country-pair fixed effects, do no fully account for this
correlation and lead to standard error estimates that are far too low. As a solution, the
authors suggest using two-way cluster-robust standard errors, developed by Cameron et al.
(2011), that cluster on country i and on country j independently for each (i,j) country pair
(Cameron and Miller, 2014).
One may be concerned about the exogeneity of the religious and ethnic measures if people
from particular cultural groups migrate in a pattern similar to trade flows. To over come
this, I use religion and ethnicity measures in 1851 (the earliest year for which both religious
and ethnic data are available) and restrict all regressions to years 1851 and later. Ethnic
and religious composition in 1851 should therefore be exogenous to relative prices in later
years.14 Moreover, individual provincial fixed effects control for all time-invariant factors of
each province in the dyad that may affect market integration (but not dyad-specific factors,
such as distance between provinces). Year fixed effects control for time trends that affect
all province-pairs uniformly. Lastly, interacting province fixed effects with year fixed effects
controls for time-varying province-specific factors. To the extent that there are no omitted
dyad-specific factors that affect market integration, the estimates may be considered causal.
I address potential omitted factors, such as railroads, in Section 6.
14
While using exogenous measures of religion and ethnicity are ideal, one may also be concerned that
religious and ethnic composition in 1851 is quite different from religious or ethnic composition in 1900. In
this case, the coefficients in equation (9) would elucidate the effect of historical cultural composition on
market integration, but would lend little insight into the effects of contemporaneous cultural composition.
There appears, however, to be insignificant changes in religious and ethnic dissimilarity between 1851 and
1900. Mean religious dissimilarity is 0.36 in both 1851 and 1900 (note that this is lower than the value
reported in the Table 6 because in 1900 Roman and Greek Catholics are reported as one religious group).
Mean ethnic dissimilarity in 1851 was 1.45 and decreased marginally to 1.42 by 1900.
18
5
Primary Results
The results in Table 7 suggest that religious differences inhibit market integration. In
Panel A, a 1 standard deviation increase in the religious Manhattan index (roughly the equivalent of moving from Dalmatia and Upper Austria’s dissimilarity to Galicia and Moravia’s
dissimilarity) is associated with a 1.1 to 1.4 standard deviation increase in relative prices
for wheat, rye, and oats. Panel B suggests that the effect of religion on market integration
operates through Roman Catholic networks. Increasing the share of the Roman Catholic
population relative to the overall population within a given provincial dyad by 1 percentage
point is associated with a 0.17 to 0.21 percent reduction in relative grain prices. There is no
evidence of a statistically significant effect of religious networks on beer or wine prices, nor
is there an identifiable relationship between ethnicity and market integration.
The results are consistent with the historical background in Section 2, which highlights
the institutional discrimination faced by religious minorities until the mid-19th century and
the potential for Catholic networks to dominate agricultural trade. Religion was an important force in market integration throughout 19th century Austria, I argue, not because of
the information-sharing effect of networks, but rather due to Catholic market power in agricultural production and the resulting price discrimination against religious minorities. The
estimates in Table 7 show that religious differences substantially inhibit market integration
in grain prices, but have little effect on beer or wine. In other words, religious networks
facilitated market integration in homogeneous goods more than differentiated goods. If
information-sharing is the mechanism through which religion facilitated market integration,
the effects should be stronger for differentiated goods (Rauch, 1999; Rauch and Trindade,
2002).
Estimating the following equation verifies the price discrimination mechanism:
ln(pit ) = α + βN on-Catholici + γN on-Germani + δXit + νt + εit
19
(10)
where ln(pit ) is the natural log of prices in province i in year t, N on-Catholici is the proportion of the population in province i that is non-Catholic in 1851, N on-Germani is the
proportion of the population in province i that is non-German in 1851, and Xit is a vector
of province-level controls, including the inverse hyperbolic sine of of population density in
year t, the distance from province i’s centroid to an ancient Roman road, the distance from
province i’s centroid to the Danube River, the ruggedness index, the soil quality index, and
per capita agricultural production in year t.15 Finally, νt are year fixed effects and εit is the
error term clustered at the province level. In some estimations I control for steam engine
use in province i in year t, in order to account for advancements in agricultural production
technology that may affect prices. Note, however, that this data is only available for the
years 1872 onward, and is likely endogenous to prices.
If price discrimination against religious minorities is the mechanism through which culture
influences market integration, β should be positive and economically significant. The results
in Table 8 confirm this relationship, suggesting that provinces with higher proportions of
non-Catholics faced higher prices. In Panel A, increasing the proportion of non-Catholics
by 1 percentage point is associated with a 0.19 percent increase in wheat prices, a 0.16
percent increase in rye prices, and a 0.50 percent increase in the price of oats. Panel B
considers changes in agricultural technology by controlling for steam engines, and produces
slightly larger magnitudes for the coefficients on grain prices and a positive and statistically
significant effect on wine prices. As a final robustness check, Panel C includes a dummy
variable equal to one if the province is Galicia, Bukovina, or Dalmatia. Galicia and Bukovina
were considered the agriculturally dominant “hinterlands” of the Habsburg monarchy, while
Dalmatia is geographically isolated from the rest of the monarchy. Although the baseline
specification in Panel A controls for geophysical factors that affect a region’s comparative
15
I run three separate price regressions for wheat, rye, and oats. The production control for the wheat
price regression is per capita wheat production. For rye and oats prices, the production controls are hence
per capita rye and oats production, respectively. Data on beer production is not available in the annual
yearbooks, while wine was only produced in the province of Dalmatia. The beer and wine price regressions
therefore do not control for production.
20
advantage in agricultural production, as well as access to historical trade routes and rivers,
which can directly affect prices through transport costs, including a fixed effect for hinterland
regions controls for other unobserved factors associated with remoteness that may affect
agricultural prices. The results in Panel C are consistent with Panel A, with the magnitude of
the coefficients on N on-Catholic increasing slightly for grain prices. There is now a negative
correlation between religion and beer prices and an economically large effect on wine prices.
These spurious effects are likely due to multicollinearity between the N on-Catholic and
Hinterland variables since the hinterlands are also the regions with the largest non-Catholic
populations.
Table 8 provides considerable evidence that regions with a higher proportion of nonCatholics faced higher prices, especially with regard to agricultural goods. These findings
are consistent with a price discrimination mechanism in which Catholic networks, which
held market power in agricultural production, charged higher prices to religious minorities,
thereby distorting market integration in agricultural trade along religious lines.
6
Potential Omitted Factors: Railroads
6.1
Railroads
The results in Table 7 may be interpreted as causal so long as there are no dyad-specific
variables relating to market integration that have been omitted from the estimation. Given
the large advancements in transportation technology that occurred throughout the 19th
century, namely the introduction of railroads, it is important to consider transport costs when
studying market integration in the Habsburg context. An existing literature substantiates
the role of transportation networks in promoting market integration in China (Baum-Snow
et al., 2015), imperial India (Donaldson, 2014), and 19th century Germany (Keller and Shiue,
2015, 2008). It is therefore possible that similar effects exist for Austria-Hungary.16
16
Schulze and Wolf (2012) examine the effect of ethnic similarities and a self-constructed measure of
railroad freight costs on grain prices for a set of Austro-Hungarian cities from 1870 to 1910 and find that
21
Prior to the railway, canals and roads were the primary mode of transportation throughout the empire, though each was highly underdeveloped. Road construction had been neglected throughout the Napoleonic period, as national finances were allocated to military
efforts. By the 1840s, the Austrian government had completed a network of imperial highways, which extended in a spoke outward from Vienna to the Austrian provinces, but failed
to reach most of Hungary and Transylvania (Blum, 1943). In 1842, average road density in
Austrian provinces was approximately 10.3 km per 1,000 square km, the majority of which
were secondary local or private roads (8.5 km per 1,000 square km) that were not equipped
for long-distance commercial trade (Austria, 1865).17 By 1910 average density in Austrian
and Hungarian provinces reached 17.4 km per 1,000 square km and was still mostly secondary roads (15.1 km per 1,000 square km) (Austria, 1865).
Water transit in the 19th century was a far more efficient mode of transportation than
road,18 but due to the mountainous and forested terrain of the region, canal and river routes
failed to develop in Austria-Hungary (Good, 1984). The Danube river, flowing from Vienna
through Budapest and out to the Black Sea, never realized commercial potential because
of geographic constraints like steep winding banks, as well as international political barriers
presented by Ottoman jurisdiction over the last 600 km of river (Blum, 1943). In addition,
no rivers extend from the Danube basin to the Adriatic Sea – the only imperial oceanic
access – severely limiting the potential for domestic and international trade by water. Large
canal projects were overlooked due to the substantial expenditures that would have been
associated with digging through the mountainous topography (Blum, 1943). The introduction of railroads, hence, offered the potential to establish significant transportation efficiency
throughout the monarchy.
both significantly reduce price dispersion between cities. Note, however, that the authors control neither
for physical distance between cities, nor for religion. Moreover, standard errors do not account for two-way
clustering, which has been shown to be important in dyadic trade regressions (Cameron et al., 2011; Cameron
and Miller, 2014).
17
These figures do not include road densities in Hungary or Transylvania since they are not available for
this time period.
18
Estimates for Austria-Hungary are difficult to come by, but in Britain it is estimated that a horse that
could carry 1 ton on land could haul 30 tons on the canal (Evans, 1981)
22
6.1.1
Railroad data
Railroad data come from the combination of imperial railroad yearbooks (Austria, 1907)
and a 1913 railroad map (Wagner and Debes, 1913) that was digitized and georeferenced
in GIS. 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. Euclidean connections between
all city-pairs were mapped in GIS in each year in order to reconstruct the Austro-Hungarian
railroad network, cross-referencing the 1913 railroad map for accuracy. The evolution of this
network is illustrated in Figure 7. A clear west-to-east development pattern is visible, with
early construction beginning around Vienna, Linz, and Prague, expanding eastward over
time.19
Railroad data was extracted to the provincial level by overlaying province boundaries
to the Euclidean railroad network and calculating a binary variable equal to 1 if a railroad
connection existed between two provinces in a given year. Although the empirical analysis
is restricted to Austrian provinces, the connection variable considers railroad paths through
the Hungarian Kingdom in order to determine whether or not two provinces have a rail link.
6.1.2
Railroad results
In order to identify the effect of railroad connectivity on market integration, I estimate
an equation similar to equation 4.2 and include a dummy variable equal to 1 if a rail link
existed in year t. Panel A of Table 9 displays OLS estimates, which suggest that a rail link
between two provinces is associated with a 3 to 4 percent reduction in relative prices. Panel B
includes individual province fixed effects in order to control for time-invariant factors of each
19
In 1841 an imperial decree ordered the construction of four national trunk lines and by 1847, over 1,600
km of track had been laid (Blum, 1943). Nonetheless, Habsburg railroad construction lagged behind other
continental railways by the mid 1800s. For instance, by 1847, there were 1,800 km of railroad track in France,
3,700 km in Britain, and 4,300 in Germany km (Blum, 1943). In 1854, the monarchy passed a railroad
concessions law, which incentivized private investment in railroad construction with interest guarantees
(Rudolph, 1976). Construction remained a private venture throughout the mid-19th century, until the great
depression (1873-1879), when the Sequestration Acts – 1878 in Austria and 1883 in Hungary – returned rail
construction to government control (Good, 1984).
23
province that may affect market integration, such as slope, elevation, and distance to river or
historical trade routes. The results still suggest that railroads increase market integration.
However, once time trends are accounted for through yearly fixed effects in Panel C, these
effects disappear, with point estimates that are actually positive for all prices except wine
and large standard errors. Lastly, Panel D replicates equation 4.2 by interacting province
and year fixed effects, now including the railroad dummy variable, and shows no evidence
of an effect of railroad connectivity on market integration. Note, however, that the effect of
religious networks remains robust to the original estimates presented in Table 7.
It is possible that there is no observed effect of railroad connectivity on trade if railroads
were built endogenously between places that were relatively more integrated. There is strong
evidence in the previous section that markets integrated across religious networks. Railroads
may therefore have been built to support trade between religiously similar regions. Table 10
explores the relationship between religion and railroad connectivity and finds that religious
networks are a strong predictor of railroad links between provinces. A 1 standard deviation
increase in the religious Manhattan index is associated with a 0.5 to 0.7 standard deviation reduction in the probability of two provinces becoming connected via the rail network.
Ethno-linguistic networks have no such effect.
The results in Table 10 have important implications. To the extent that transportation
networks are built between places that are integrated for other reasons, such as culture, large
and expensive infrastructure projects may not realize their anticipated gains. In this sense,
the results for Austria are similar to Fogel (1964), who shows limited impacts of American
railroads on economic growth in the 19th century. More importantly, the empirical exercise
above illustrates that investment in transportation infrastructure may actually reinforce
existing trade-diverting networks, thereby distorting efficiency rather than promoting it.
Although the analysis is concerned with religion, the findings are related to the literature on
ethnic favoritism and economic development, which shows that investment in public goods
and economic growth are hampered by the political and economic favoritism of one group
24
over another. (Easterly and Levine, 1997; Alesina and Ferrara, 2005; Miguel and Gugerty,
2005; Burgess et al., 2015).
7
Conclusion
Cultural networks play an important role in trade. To the extent that they overcome
information constraints, these networks can be trade-enhancing. Sometimes, however, they
create persistent barriers. This paper explores the effect of culture on market integration
in 19th century Austria and finds that religious networks were important forces in market
integration, not through their ability to overcome informational barriers to trade, but rather
through price discrimination against religious minorities. This mechanism is linked to the
institutional discrimination in land rights against religious minorities since the inception of
the Habsburg monarchy, leading to Roman Catholic market power in agricultural production.
Improvements in transportation infrastructure failed to promote market integration, since
connections were established between religiously similar places, thereby reinforcing these
trade-diverting effects of religious networks. These findings have important implications for
contemporary regional economies in which a dominant cultural group owns the means to
production.
25
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29
8
8.1
Figures and Tables
Figures
Figure 1: Austro-Hungarian Empire in the 19th Century
0
50 100
200 Kilometers
Austrian Provinces
Hungarian Provinces
Sources: Esri, HERE, DeLorme, Intermap, increment P Corp., GEBCO, USGS, FAO, NPS, NRCAN, GeoBase, IGN,
Figure 2: Austrian Provinces in the 19th Century
Galicia
Silesia
Bohemia
Moravia
Bukovina
Vienna
^
Lower Austria
Upper Austria
Salzburg
Styria
Tyrol
Carinthia
Carniola
Kustenland
Dalmatia
30
Figure 3: Land Inequality and Religion (1857)
(b) Area per owner
40
30
Land Area per Owner
10
20
.3
.2
.1
% Population that Own Land
.4
50
(a) % of population owning land
0
.2
.4
.6
.8
1
0
.2
.4
% Non−Catholic
.6
.8
1
% Non−Catholic
Figure 4: Extensive Form Game
Nature
µ
1−µ
S
not supply
screen
supply
screen
not supply
supply
C
0
0 + δV N
0
−c + δV N
1
−1 + δV N
U
0 + δV
0 + δV N
0 + δV C
−c + δV S
not trade
31
0 + δV U
0 + δV N
not pay
1 + δV U
−1 + δV N
pay
β + δV C
α + δV S
Figure 5: Relative Prices
(b) Rye
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
(a) Wheat
1820
1840
1860
1880
lpoly smoothing grid
95% CI
1900
1920
1820
1840
Relative price: Wheat
1860
1880
lpoly smoothing grid
95% CI
1920
1.6
1.5
1.4
1.3
1.2
1.1
1
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
(d) Beer
1.7
(c) Oats
1900
Relative price: Rye
1820
1840
1860
1880
lpoly smoothing grid
95% CI
1900
1920
1820
Relative price: Oats
1840
1860
lpoly smoothing grid
95% CI
1880
1900
Relative price: Beer
Figure 6: Cultural Diversity (1851)
(b) Religion
(a) Ethnicity
Non-GermanPopulation Shares
Non-Catholic Population Shares
0 - 5%
5 - 25%
25 - 50%
50 - 75%
75 - 100%
0 - 2%
2 - 5%
5 - 25%
25 - 55%
55 - 100%
32
Figure 7: Railroad Network: 1845-1905
(b) 1865
(a) 1845
Austrian Provinces (1800)
Hungarian Provinces (1800)
0
80
160
Austrian Provinces (1800)
Hungarian Provinces (1800)
320 Kilometers
0
80
160
320 Kilometers
(c) 1885
(d) 1905
Austrian Provinces (1800)
Hungarian Provinces (1800)
0
80
160
Austrian Provinces (1800)
Hungarian Provinces (1800)
320 Kilometers
0
33
80
160
320 Kilometers
8.2
Tables
Table 1: Ethnicity (shares of total population in 1851)
34
Lower Austria
Upper Austria
Salzburg
Styria
Carinthia
Carniola
Kustenland
Tyrol
Bohemia
Moravia
Silesia
Galicia
Bukovina
Dalmatia
German
98.5
100.0
100.0
63.8
70.0
8.1
2.5
61.6
38.6
27.6
47.8
2.0
6.7
0.0
Czech
0.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
59.8
70.2
20.1
0.0
0.5
0.0
Polish
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
31.5
40.9
1.1
0.0
Ruthenian
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
50.1
38.1
0.0
Slovene
0.0
0.0
0.0
36.2
30.0
88.1
37.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Croat
0.4
0.0
0.0
0.0
0.0
3.8
16.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Serb
0.0
0.0
0.0
0.0
0.0
0.0
8.1
0.0
0.0
0.0
0.0
0.0
0.0
96.2
Hungarian
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.5
0.0
Italian
0.0
0.0
0.0
0.0
0.0
0.0
34.4
38.3
0.0
0.0
0.0
0.0
0.0
3.5
Romanian
0.0
0.0
0.0
0.0
0.0
0.0
0.5
0.0
0.0
0.0
0.0
0.0
48.5
0.0
Albanian
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
Greek
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.6
0.0
Jewish
0.3
0.0
0.0
0.0
0.0
0.0
0.9
0.1
1.6
2.1
0.6
6.9
3.1
0.1
Table 2: Religion (shares of total population in 1851)
Lower Austria
Upper Austria
Salzburg
Styria
Carinthia
Carniola
Kustenland
Tyrol
Bohemia
Moravia
Silesia
Galicia
Bukovina
Dalmatia
Roman
Catholic
98.7
97.4
99.9
99.4
94.4
99.9
98.4
99.9
96.3
95.0
85.6
45.4
9.1
80.9
Greek
Catholic
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
46.8
2.6
0.1
35
Orthodox
Protestant
Jewish
0.0
0.0
0.0
0.0
0.0
0.1
0.4
0.0
0.0
0.0
0.0
0.0
82.6
18.9
0.9
2.6
0.1
0.6
5.6
0.0
0.3
0.0
2.0
2.9
13.9
0.5
1.9
0.0
0.4
0.0
0.0
0.0
0.0
0.0
0.9
0.1
1.7
2.1
0.6
7.3
3.8
0.1
Table 3: Ethnic Dissimilarity (1851)
36
Upper Austria
Salzburg
Styria
Carinthia
Carniola
Kustenland
Tyrol
Bohemia
Moravia
Silesia
Galicia
Bukovina
Dalmatia
Lower
Austria
0.030
0.030
0.723
0.600
1.829
1.935
0.766
1.206
1.425
1.023
1.953
1.850
1.996
Upper
Austria
0.000
0.723
0.600
1.838
1.949
0.768
1.228
1.447
1.045
1.959
1.866
1.998
Salzburg
Styria
Carinthia
Carniola
0.723
0.600
1.838
1.949
0.768
1.228
1.447
1.045
1.959
1.866
1.998
0.123
1.115
1.226
0.768
1.228
1.447
1.045
1.959
1.866
1.998
1.238
1.349
0.768
1.228
1.447
1.045
1.959
1.866
1.998
1.128
1.838
1.838
1.837
1.838
1.959
1.866
1.765
Kustenland
1.44
1.931
1.930
1.936
1.940
1.921
1.927
Tyrol
Bohemia
Moravia
Silesia
Galicia
Bukovina
1.225
1.445
1.042
1.957
1.863
1.996
0.219
0.813
1.927
1.824
1.996
1.033
1.917
1.814
1.996
1.316
1.822
1.996
1.113
1.996
1.996
Table 4: Religious Dissimilarity (1851)
37
Upper Austria
Salzburg
Styria
Carinthia
Carniola
Kustenland
Tyrol
Bohemia
Moravia
Silesia
Galicia
Bukovina
Dalmatia
Lower
Austria
0.015
0.019
0.011
0.099
0.021
0.021
0.017
0.045
0.071
0.257
1.049
1.750
0.383
Upper
Austria
0.030
0.022
0.084
0.032
0.028
0.032
0.034
0.060
0.246
1.054
1.739
0.386
Salzburg
Styria
Carinthia
Carniola
0.008
0.114
0.002
0.028
0.004
0.064
0.090
0.276
1.060
1.769
0.386
0.106
0.010
0.026
0.010
0.056
0.082
0.268
1.054
1.761
0.386
0.116
0.112
0.116
0.084
0.060
0.162
1.054
1.737
0.386
0.030
0.004
0.066
0.092
0.278
1.062
1.771
0.386
Kustenland
0.026
0.048
0.074
0.264
1.044
1.741
0.372
Tyrol
Bohemia
Moravia
Silesia
Galicia
Bukovina
0.062
0.088
0.274
1.058
1.767
0.384
0.026
0.236
1.020
1.705
0.384
0.212
1.018
1.701
0.384
1.044
1.727
0.384
1.629
1.060
1.385
Table 5: Primary School Enrollment (1851)
(1)
(2)
(3)
% Non-Catholic (1851)
-0.328***
(0.082)
-0.282***
(0.056)
-0.262***
(0.078)
% Non-German (1851)
0.041
(0.048)
0.032
(0.048)
0.035
(0.050)
-0.002
(0.012)
-0.003
(0.012)
ln(Steam Engine Tests)
Hinterland
Year Fixed Effects
Years
Observations
-0.021
(0.052)
yes
1851-1913
544
yes
1872-1913
392
yes
1872-1913
392
Unit of observation is a province. Standard errors are in parentheses and
clustered at the province level. Additional controls: soil quality, km to
Roman road, km to the Danube, ln(population density), and ruggedness.
* p< 0.10, ** p<0.05, *** p < 0.01.
38
Table 6: Summary Statistics
N
Mean
SD
Min
Max
Province-Level Variables
Price: wheat
Price: rye
Price: oats
Price: beer
Price: wine
Production (Hl/person): wheat
Production (Hl/person): rye
Production (Hl/person): oats
% Non-Catholic (1851)
% Non-German (1851)
Soil quality
Population density
Km to Roman road
Steam engines
766
760
757
651
672
541
538
537
840
840
840
838
840
466
21.84
17.26
17.12
0.33
0.96
0.49
0.82
0.97
0.14
.55
0.52
67.89
102.42
2072.02
4.90
4.02
4.67
0.11
0.40
0.27
0.58
0.77
0.25
0.37
0.14
32.90
129.02
3964.05
9.90
6.23
5.45
0.13
0.13
0.03
0.01
0.02
0.00
0.00
0.27
18.60
1.03
2.00
37.90
32.45
40.68
0.69
3.15
2.63
3.20
6.97
0.91
1.00
0.77
174.70
369.88
25734.00
Province-Pair Variables
Relative price: wheat
Relative price: rye
Relative price: oats
Relative price: beer
Relative price: wine
Religious Dissimilarity (1851)
Ethnic dissimilarity (1851)
% Catholic (1851) in dyad
Rail connection (0/1)
Average soil quality
Average ruggedness
Km between i and j
4645
4568
4573
4016
4283
5551
5551
5551
5551
5551
5551
5551
1.18
1.22
1.31
1.39
1.83
0.49
1.45
0.85
0.56
0.52
288.62
439.00
0.21
0.26
0.36
0.35
1.18
0.61
0.56
0.19
0.50
0.09
128.13
259.14
1.00
1.00
1.00
1.00
1.00
0.00
0.00
0.34
0.00
0.30
101.45
86.93
2.86
3.16
4.43
2.97
11.95
1.82
2.00
1.00
1.00
0.73
661.15
1093.97
Summary statistics restricted to the years 1851 to 1910.
39
Table 7: Culture and Relative Prices
|ln(pi ) − ln(pj )|
(1)
Wheat
(2)
Rye
(3)
Oats
(4)
Beer
(5)
Wine
0.328***
(0.057)
[1.37]
0.304***
(0.051)
[1.11]
0.419***
(0.042)
[1.20]
0.120
(0.406)
[0.26]
0.199
(0.227)
[1.08]
-0.019
(0.018)
0.019
(0.018)
-0.028
(0.030)
0.159
(0.104)
-0.021
(0.238)
Panel A: Religious dissimilarity
Religious Dissimilarity (1851)
Ethnic dissimilarity (1851)
Panel B: Roman Catholic networks
% Catholic (1851) in dyad
-0.189**
(0.080)
[-0.27]
-0.166**
(0.073)
[-0.22]
-0.206**
(0.095)
[-0.20]
0.100
(0.194)
[0.06]
-0.235
(0.480)
[-0.19]
Ethnic dissimilarity (1851)
-0.006
(0.016)
0.030
(0.019)
-0.014
(0.037)
0.153
(0.109)
-0.006
(0.216)
yes
yes
3414
yes
yes
3414
yes
yes
3414
yes
yes
3414
yes
yes
3414
Province i x year FE
Province j x year FE
Observations
Unit of observation is a province-pair for the years 1851-1893. Additional controls include:
average soil quality, average ruggedness, and ln(km between i and j). Standard errors
in parentheses and clustered using two-way cluster robust standard errors (Cameron, Gelbach,
and Miller, 2011). Standardized beta coefficients in brackets.
* p< 0.10, ** p<0.05, *** p < 0.01.
40
Table 8: Culture and Price Discrimination
ln(price)
(1)
Wheat
(2)
Rye
(3)
Oats
(4)
Beer
(5)
Wine
Panel A: Baseline specification (1851-1893)
% Non-Catholic (1851)
0.187***
(0.061)
0.157*
(0.087)
0.489***
(0.134)
-0.204
(0.236)
0.599
(0.362)
% Non-German (1851)
0.014
(0.030)
0.120*
(0.059)
0.123
(0.123)
-0.039
(0.181)
-1.482***
(0.299)
298
yes
298
yes
298
yes
298
yes
298
yes
Observations
Year Fixed Effects
Panel B: Technology controls (1872-1893)
% Non-Catholic (1851)
0.258***
(0.069)
0.202*
(0.108)
0.588***
(0.152)
-0.118
(0.234)
0.690**
(0.292)
% Non-German (1851)
0.000
(0.035)
0.165*
(0.079)
0.097
(0.151)
-0.107
(0.183)
-1.529***
(0.243)
261
yes
261
yes
261
yes
261
yes
261
yes
Observations
Year Fixed Effects
Panel C: Hinterland controls (1851-1893)
% Non-Catholic (1851)
0.200**
(0.072)
0.258**
(0.114)
0.476**
(0.199)
-0.881***
(0.104)
1.231**
(0.443)
% Non-German (1851)
0.014
(0.029)
0.111**
(0.048)
0.125
(0.125)
-0.054
(0.098)
-1.468***
(0.299)
Hinterland
-0.013
(0.057)
-0.093
(0.086)
0.014
(0.166)
0.654***
(0.086)
-0.611*
(0.345)
298
yes
298
yes
298
yes
298
yes
298
yes
Observations
Year Fixed Effects
Unit of observation is a province. Standard errors are in parentheses and clustered at
the province level. Panels B and C control for steam engines. All panels control for: soil
quality, ln(population density), km to Roman road, km to the Danube, and ruggedness.
Columns (1)-(3) include productivity controls: production per capita 1851 for wheat, rye,
and oats, respectively. Columns (4) and (5) have no productivity controls, since they are
unavailable. * p< 0.10, ** p<0.05, *** p < 0.01.
41
Table 9: Railroads and Market Integration
(1)
Wheat
(2)
Rye
(3)
Oats
(4)
Beer
(5)
Wine
-0.020*
(0.011)
-0.025***
(0.006)
-0.013
(0.033)
-0.018
(0.021)
-0.159
(0.109)
0.161***
(0.022)
0.159***
(0.021)
0.237***
(0.027)
-0.054
(0.038)
-0.179
(0.127)
Rail connection (0/1)
-0.031**
(0.015)
-0.040***
(0.010)
-0.028
(0.028)
0.010
(0.022)
-0.062
(0.039)
Religious Dissimilarity (1851)
0.186***
(0.019)
0.136***
(0.019)
0.273***
(0.038)
-0.008
(0.333)
0.295*
(0.173)
0.059
(0.058)
0.065
(0.066)
0.008
(0.052)
0.032*
(0.019)
-0.112***
(0.043)
0.226***
(0.036)
0.183***
(0.040)
0.289***
(0.040)
0.002
(0.337)
0.274
(0.178)
0.016
(0.023)
0.023
(0.027)
0.025
(0.030)
-0.008
(0.049)
-0.046
(0.093)
0.332***
(0.058)
0.311***
(0.055)
0.425***
(0.048)
0.118
(0.402)
0.186
(0.221)
3414
3414
3414
3414
3414
|ln(pi ) − ln(pj )|
Panel A: OLS
Rail connection (0/1)
Religious Dissimilarity (1851)
Panel B: Province Fixed Effects
Panel C: Province and Year Fixed Effects
Rail connection (0/1)
Religious Dissimilarity (1851)
Panel D: Province x Year Fixed Effects
Rail connection (0/1)
Religious Dissimilarity (1851)
Observations
Unit of observation is a province-pair for the years 1851-1893. Additional controls: ethnic
dissimilarity (1851), average soil quality, average ruggedness, and ln(km between i and j).
Standard errors in parentheses and clustered using two-way cluster robust standard errors
(Cameron, Gelbach, and Miller, 2011). * p< 0.10, ** p<0.05, *** p < 0.01.
42
Table 10: Religion and Railroads
(1)
Connect (0/1)
Religious dissimilarity (1831)
-0.429***
(0.095)
Religious dissimilarity (1851)
-0.593***
(0.117)
Ethnic dissimilarity (1851)
Province i x year FE
Province j x year FE
Years
Observations
(2)
Connect (0/1)
0.074
(0.076)
yes
yes
1831-1910
7371
yes
yes
1851-1910
5551
Unit of observation is a province-pair. Additional controls include:
average soil quality, average ruggedness, and ln(km between i and j).
Standard errors in parentheses and clustered using multi-way clustering
(Cameron, Gelbach, and Miller, 2011).
* p< 0.10, ** p<0.05, *** p < 0.01.
43