inefficiency, fees, and profits: a cross-country analysis...
TRANSCRIPT
Inefficiency, Fees, and Profits: A Cross-Country Analysis of Railroad
Performance, 1880-1912
Dan Bogart1 Department of Economics, UC Irvine
3151 Social Science Plaza Irvine CA 92697-5100
Abstract Railroads revolutionized travel, trade, and business. This paper estimates and compares railroad fees, profits, and cost inefficiency across countries from 1880 to 1912. It shows that fees declined for most countries, while profits were more stable. Cost inefficiency varied significantly across countries and over time. The U.S., France, and Germany had the greatest reductions in inefficiency and were the most efficient by the 1900s. A cross-country regression analysis also shows that lower inefficiency reduced fees and raised profits, but its effect was small in comparison to other factors. The findings illuminate the comparative performance of a leading sector in the world economy at the turn of the twentieth century. Keywords: Railroads; Infrastructure Performance; Cost Inefficiency; Stochastic Frontier Estimation; Globalization JEL Classifications: N40, N70, L92, P52
1 I would like to thank seminar participants at IMT Lucca, Warwick University, and LSE for comments on earlier versions of this paper. I would also like to thank Jan-Luiten Van Zanden, David Good, Michael Mesch, Max-Stephan Schulze, Roman Studer, Petra Gerlach, Steve Nafziger, Giovanni Federico, and Ola Grytten for providing invaluable help in locating data. Finally I would like to thank Quan Trinh, Sarah Chiu, and Cecilia Lanata Briones for valuable research assistance.
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1. Introduction
The performance of the railroad sector was a major economic issue around the
world at the turn of the twentieth century. Contemporaries were well aware that railroads
had the potential to revolutionize trade, travel, and business. Great attention was paid to
size of the railroad network, but increasingly observers were focusing on the level of fees
and profits.2 Passenger fares affected the accessibility of rail travel to the general public.
Freight charges influenced the degree to which railroads changed patterns of trade, urban
structure, and agriculture settlement. Profitability influenced investment and the
institutions which supported the financing of railroads.
Inefficiency was also a major issue in the railroad sector at the turn of the century.
Among other things inefficiency was associated with failures to maintain the fixed and
rolling stock, failures to adapt to the changing volume and mix of traffic, and failures to
rationalize services. 3 Like today, inefficiency had important implications for the railroad
sector at the time. It implied that operating costs could have been lower without
changing technology or increasing scale. The resulting savings could have been passed
along to users in the form of lower fees or they might have been distributed to investors
in the form of higher profits.
This paper estimates and compares the level of fees, profits, and inefficiency
across countries from 1880 to 1912. It uses a new cross-country data set with
information on total costs, revenues, and output for the railroad sector as a whole. The
data cover several of the largest economies including the Netherlands, Canada, Russia,
Switzerland, Sweden, Britain, Denmark, Portugal, Austria, Japan, Argentina, Australia,
2 See for example Raper (1912) who compared railroads in the United States with Western Europe. He emphasizes the development of lines but more so the level of railroad rates and services. 3 See Meyer (1906) and Raper (1912) for an early assessment of railroad inefficiency.
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India, the U.S., Belgium, Italy, Germany, Spain, France, and Norway. It establishes
which of these countries had the lowest fares, freight charges, operating margins, and cost
inefficiency from 1880 to 1912 and how these performance measures evolved over time.
Real fares per passenger mile and freight charges per ton mile are estimated using
average passenger and freight revenues per unit-mile deflated by a consumer price index.
The data show that the ranking of countries in terms of fares was relatively stable from
the 1880s to the 1900s. The same is true of the rankings of freight charges, but there was
little overlap in the rankings by fares compared to freight charges. Belgium, Canada, and
the U.S. had the lowest freight charges; Italy and Switzerland had the highest freight
charges. India, Russia, Belgium, and Japan had the lowest passenger fares; the U.S. had
the highest passenger fares. Across all countries fares and freight charges declined
substantially. Real freight charges declined by 31% from the 1880s to the 1900s and
fares declined by 24%. In the U.S—which had the largest railroad sector in the world—
real freight charges declined by 38% and real passenger fares declined by 22%.
Profitability is measured by the operating margin and the rate of return on capital
invested in the railroad network. Both rankings of profitability were relatively stable
across time and were correlated with one another. Japan and India had some of the
highest operating margins and rates of return in the 1900s. Italy, Norway, and Canada
had some of the lowest operating margins and rates of return. Across countries operating
margins were relatively stable while rates of return increased. The change was largely
driven by the stable margins and higher rates of return in the U.S. The turn of the century
was generally an age of prosperity for the U.S. railroad investor.
3
The estimation of cost inefficiency poses a more challenging problem because it
is not directly observable. Following the standard approach in the literature, this paper
estimates a cost function and assumes that inefficiency is represented by a half-normal
random variable whose variance is estimated along with a normally distributed error
term. Country fixed effects are also incorporated to separate inefficiency from
unobserved heterogeneity. The results show that the rankings of inefficiency changed
substantially across time. Switzerland and Britain had the least inefficient (i.e. most
efficient) railroad sectors in the 1880s; Argentina and Canada were least inefficient in the
1890s; France, Spain, and the U.S. were least inefficient in the 1900s. Average cost
inefficiency decreased by 12% across all countries. The downward trend was largely due
to efficiency improvements in the U.S. Elsewhere inefficiency typically increased.
The last part of the paper examines the effects of cost efficiency on fees and
profits. A panel regression with country fixed effects, year fixed effects, and country-
specific time trends shows that lower inefficiency reduced fares and freight charges. A
similar analysis shows that lower inefficiency increased operating margins and rates of
return. The quantitative impact of efficiency change was relatively small however. Most
of the differences in fees and profits were due to technological change, greater scale, and
country-specific factors, like geography.
This paper is not the first to examine railroad performance in historical
perspective. Crafts, Mills, and Mulatu (2007), Crafts, Leunig, and Mulatu (2008), Leunig
(2006), and Arnold and McCartney (2005) estimate freight charges, fares, travel speeds,
rates of return, productivity, and cost inefficiency for British railways before 1912.
Herranz-Loncan (2006) estimates productivity growth in Spanish railroads over the same
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period. Foreman-Peck (1907) studies construction costs and Millward (2004) focuses on
network development and ownership policies in a cross-country setting. This paper adds
to this literature by examines fees, profits, and inefficiency across countries.
The paper also relates to the large literature on efficiency estimation.4 One
example is the work by Farsi et. al. (2005) who use stochastic frontier models to estimate
cost inefficiency for Swiss railroads in the 1980s and 1990s. Several studies have applied
stochastic frontier models to cross-country data. Greene (2004) estimates efficiency in
national health care systems using a model that controls for unobserved heterogeneity.
This paper follows a similar approach using what is known in the literature as the ‘true’
fixed effects model.
Lastly the paper adds to the broader literature on transportation improvements and
economic development. Railroads were inter-connected with the expansion of
international trade, international finance, and industrialization during the first era of
globalization. There is growing interest in studying the effects of railroads in a
comparative context. The appendix provides series on average passenger fares and
freight charges which will be useful to scholars engaged in these studies.
The paper is organized as follows. Section 2 reviews the data sources. Sections 3
and 4 present the cross country estimates of fees, profits, and inefficiency. Section 5
examines the effect of inefficiency on fees and profits. Section 6 concludes.
2. Data
Aggregate railroad statistics are available in a number of sources. The main two
are The Statistical Abstract for the Principal and Other Foreign Countries and The 4 See Kumbhaker and Lovell (2000) for an overview of the literature on efficiency estimation.
5
Statistical Abstract for the Several Colonial and other Possessions of the United
Kingdom both of which were published by the British Board of Trade starting in the
1870s. The data in the Statistical Abstracts were drawn from publications of annual
railway statistics in each country. These volumes marked one of the first attempts to
compile and present comparable cross-national data. The information is clearly presented
and is easily entered into spreadsheets for statistical analysis.5 The main variables used
in this paper are total railroad miles open, total revenues, total expenses, construction
costs, the number of passengers, passenger miles, the number of tons shipped, and ton
miles.
Passenger miles and ton miles are the output variables. For most countries,
passenger miles are calculated by multiplying the number of passengers transported by
the average trip in miles; ton miles are calculated by multiplying tons shipped by the
average haul in miles. Unfortunately the average haul or the average trip was not reported
in some countries. In these cases a series of steps were performed. First, the
International Historical Statistics volumes were consulted to check for published series
on passenger miles and ton miles (see Mitchell 1988, 1992, 1995, 2003). Second, if
necessary, the data on average haul and average trip was interpolated for missing years
and then multiplied by the number of passengers or tons. The average haul and average
trip length was strikingly stable in most advanced countries at the turn of the twentieth
century. For example, the coefficients of variation for the average trip length and average
haul in Germany between 1901 and 1912 are 0.01 and 0.008 respectively. The stability
of the average trip length and average haul length implies that little error is introduced by
5 Additional information was obtained in the Statistical Abstract for the United Kingdom, the Statistical Abstract of the United States, and Estadistica de Los Ferrocarriles en Explotacion.
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interpolating to missing years. The third and final step was to consult the secondary
literature for additional estimates of output when the Statistical Abstracts provide no
information on average haul or average trip length. In one important case, Crafts, Mills,
and Mulatu (2007) provide estimates of ton miles and passenger miles for British
railways.
Total revenues and total expenses are the other main variables used in the
analysis. Both variables are reported in the domestic currency and are converted into
British pounds using the official exchange rate published in the Statistical Abstracts.6 The
series are then deflated using a domestic consumer price index with base year 1905.
Consumer price indices for each country are taken from Global financial data.7
Total revenues are divided into revenues from passengers, freight, and other
sources. Total passenger revenues divided by passenger miles provides an estimate of the
average fare per passenger mile. Similarly total freight revenues divided by ton miles
provides a measure of the average freight charge per ton mile.8 Fares and freight charges
in each country are converted into pence using an exchange rate for British pounds. They
are then deflated using a domestic consumer price index with base year 1905.
Total expenses are used as the primary measure of costs. It includes spending on
the operation of railroads, such as labor and fuel, spending on maintenance to the track,
plant, and equipment, and lastly purchases of new capital goods like locomotives.
Construction spending is usually provided separately and is never included in total 6 For example, the official exchange rate between the U.S. dollar and the pound was 0.20833 throughout the sample period 7 See https://www.globalfinancialdata.com/ for more information. Canadian consumer prices are from Minns and MacKinnon (2006) because they are not reported in GFD. 8 A comparison between fares reported in secondary sources and average fare per passenger mile shows that the two are in close correspondence. For example, see Strong (1904, p. 133) which reports fares for the U.S., England, France, Germany, Austria, and Belgium. The advantage of using average fares and average freight charges is that they can be computed for many countries across years.
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expenses. Expenditures on maintenance and operation represent the vast majority of total
expenses. Purchases of new capital goods, like locomotives, are relatively minor. Total
expenses thus capture the operational expenses of railroads. They can be deducted from
total revenues to yield an estimate of operating profits (or net receipts in the railroad
literature).
There were four countries where working expenses (i.e. total expenses less
spending on new capital goods) are reported, but not total expenses.9 In these cases it is
necessary to estimate total expenses using data from countries that report both working
and total expenses. Table 1 shows that in a sample of countries the average ratio of
working to total expenses was 0.941. One possibility is to multiply working expenses by
the inverse of the average ratio of working to total expenses in table 1. This approach
might lead to a biased estimate of total expenses if the ratio of working to total expenses
varied systematically with the growth of the network. A regression of the ratio of
working to total expenses in country i and year t on the growth of railroad miles in
country i and year t shows there is a negative relationship.10 Building on this finding,
working expenses were multiplied by the inverse of the predicted ratio of working to total
expenses using the regression estimates.
The Statistical Abstracts do not separate expenditures on inputs, nor do they
provide data on input prices. A number of secondary and primary sources were used to
estimate the prices of the three main inputs—labor, fuel, and capital. Railroads hired
workers from all regions of the country and would have paid something similar to the
average wage in the country. Williamson (1995) provides real wage indices for Norway,
9 Working expenses are reported for Britain, Australia, Canada, and India. 10 The predicted ratio of working to total expenses in year t equals 0.9444897 - 0.0763076*Mileage Growth in year t.
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Sweden, the Netherlands, France, Belgium, Spain, Italy, the U.S., Argentina, Britain,
Germany, Australia, and Canada. Williamson reports the 1905 wage rate in shillings per
week, calculated using exchange rates between the British pound and the domestic
currency.11 The 1905 weekly wage rate was then multiplied by Williamson’s real wage
index based in 1905 to arrive at an estimate of the real weekly wage in all other years.
For other countries in the sample individual sources were consulted. Studer
(2004) provides weekly real wages for Swiss workers along the lines of Williamson’s
estimates. The Russian weekly wage in 1905 was estimated to be 3 rubles or 6.67
shillings.12 The weekly real wage series was then constructed using Gregory’s wage
index for Russian railroad workers and a retail price index with base year 1905. Mesch
(1984) provides real weekly wages for Austria starting in 1891. The weekly wage is
converted into 1905 pounds using the exchange rate. Monthly wages for India are
available in the Statistical Abstracts for British India starting in 1884. The wage was
converted to shillings per week and then divided by the Indian consumer price index with
base year 1905.13 Lastly, daily wages for agricultural workers in Japan from 1885 to
1913 were provided by David Jacks, Peter Lindert, and Salvadore Puente through the
Global Price and Income History Group and are based on the Financial and Economic
Annual of Japan.14 Like India, the wage was converted to shillings per week and then
divided by the Japanese consumer price index based in 1905.
11 For Canada the weekly wage in shillings was calculated using data from Mackinnon (1996). For Norway the weekly wage was assumed to be 88% of the weekly wage for Sweden. The 88% figures is based on Williamson’s estimate that PPP adjusted real wages for Norway were 88% of Sweden in 1905. 12 The Russian wage is taken from http://www.shtetlinks.jewishgen.org/Lida-District/wages.htm. 13 The Indian consumer price index is taken from Gobal financial data https://www.globalfinancialdata.com 14 See http://gpih.ucdavis.edu/.
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Coal prices are available for many countries in the Returns Relating to the
Production and Consumption of Coal published by the British Board of trade.15 The
reports list the price of coal in shillings per metric ton for Britain, Russia, Sweden,
Germany, Belgium, France, Spain, Austria, Hungary, Japan, the U.S., India, Canada, and
Australia.16 Dutch coal prices are provided by Arthur van Riel through the Global Price
and Income History Group.17 Norwegian coal prices are published in Siegenthaler
(1996). Italian coal prices are published in Cianci (1933). Finally, Argentine coal prices
paid by railroads are published in the annual bulletin, Estadistica de Los Ferrocarriles en
Explotacion. All coal prices are deflated by domestic consumer price indices with base
year 1905.
There are no comprehensive sources on the price of capital goods in the railroad
sector. Collins and Williamson (2001) provide data on capital prices for a handful of
countries but they are too few to cover the countries here and may not be applicable to
the railroad sector. As a proxy for capital prices this paper uses the average construction
cost per railroad mile in each year. Materials for repairs to the track and stations were the
same as for construction of the track and station, namely timber, iron, and steel.
Therefore movements in the average cost of construction should proxy for movements in
the average price of capital goods used for repairs. Construction costs are published in
the Statistical Abstracts for most countries. The average cost per mile was converted into
British pounds using exchange rates and divided by a domestic consumer price index
with base year 1905. For Spain and Russia total construction costs in 1880 and 1888
15 See House of Commons, Parliamentary Papers, 1895, LXXXVIII p.105 for one of the volumes in this series 16 In most countries, coal prices are the pit’s mouth (i.e. wholesale) but in a few cases they are reported as market prices. 17 See http://iisg.nl/hpw/data.php#netherlands for more details.
10
were combined with annual investment expenditures from Herraz-Locan (2005) and
Gregory (1982) to yield an estimate of annual construction costs.18
3. Cross-Country Comparisons of Railroad Performance
3.1 Fares and Freight Charges
This sub-section establishes which countries had the lowest passenger fares and
freight charges from 1880 to 1912 and how they evolved over time. The next sub-section
reports the same for operating margins and rates of return. In the appendix several tables
list fees and profits in each country beginning with the earliest observations in the 1870s.
Table 2 lists real fares per passenger mile in 1905 British pence averaged over the
1880s, 1890s, and 1900s separately. It also lists the rankings of countries in terms of
fares. Several patterns emerge from the data. First, the ranking of passenger fares was
quite stable over time. The correlation coefficient of the country rankings in the 1890s
and 1900s is 0.93 indicating that countries which had the lowest fares in the 1890s also
had the lowest fares in the 1900s. The correlation coefficient is slightly lower for the
rankings in the 1880s and 1900s indicating there were some movements over the longer
time span. Britain, for example, had fares in the mid-range of other countries in the
1880s but they were in the higher range by the 1900s.
India had the lowest passenger fares among all countries by a substantial margin.
Its fare was around 0.2 pence per passenger mile in the 1900s. A second group of
countries, including Russia, Belgium, and Japan also had low fares ranging between 0.3
and 0.35 pence in the 1900s. A third group of middle-European countries, Austria,
Hungary, and Germany, had slightly higher fares ranging between 0.48 and 0.51. Italy 18 Data on construction costs in 1880 and 1888 are taken from Mulhall (1892).
11
and Norway followed with fares ranging around 0.63. Switzerland, the Netherlands,
Sweden, Australia, France, and Britain all had fares ranging between 0.7 and 0.78.
Lastly, Argentina and the U.S. had the highest fares at around 1pence per mile in the
1900s.
The second general pattern is that real fares declined from the 1880s to the 1900s.
The cross-country average in each decade weights fares by the number of passenger
miles. The weighted average of fares in the 1880s was 0.85 pence. In the 1900s it
decreased to 0.64 pence or a 24% decline.
Table 3 lists real freight charges per ton mile in 1905 pence and the rankings of
countries. Some of the same patterns apply to freight charges as fares. The correlation
coefficients for the rankings in the 1890s and the 1900s and the rankings in the 1880s and
the 1900s are above 0.9. Few countries made significant movements up or down in the
rankings of freight charges or fares.
The ranking of countries by freight charges was quite different from the rankings
by fares. The correlation coefficient of the two in the 1900s is only 0.28. Countries with
low freight charges did not necessarily have low fares. Belgium had the lowest freight
charges in all periods. They were around 0.28 pence per ton mile in the 1880s and rose
slightly to 0.3 pence in the 1900s. Canada, the U.S., Russia, India, and Japan were a
second group with freight charges ranging from 0.36 to 0.47 pence. Except for Japan all
of them had large agricultural exports, like wheat. Low freight charges were thus
intimately connected with their global production. The Netherlands, Hungary, Austria,
Germany, France and Sweden were in a third tier with freight charges ranging between
0.61 and 0.75. Argentina, Norway, Britain, and Australia followed with freight charges
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between 0.95 and 1.08. Lastly, Italy and Switzerland had the highest freight charges at
1.23 and 1.38 pence respectively.
Across all countries real freight charges declined significantly. The average
freight charge weighted by ton miles was 0.69 pence per ton mile in the 1880s. It
decreased to 0.48 pence in the 1900s representing a 31% decline. The freight charge
average is heavily influenced by the U.S. which represented 60% of the ton miles in the
sample. If the U.S. is dropped then average freight charges declined by only 22%.
Overall it is clear that both shippers and passengers enjoyed much lower fees in the 1900s
as compared with the 1880s. The gains to the global economy were substantial as many
scholars have shown.19
3.2 Operating Margins and Rates of Return
Profitability can be measured through operating margins or the rate of return.
Operating margins equal profits divided by revenues. The rate of return equals profits
divided by the construction cost of the railroad network. The former measures profits per
unit of revenue while the latter measures profits per unit of investment. Table 4 lists
operating margins in percentages averaged over the 1880s, 1890s, and 1900s. It also lists
the rankings of countries in the same decades. As with fees, the rankings of operating
margins across countries were relatively stable over time. The correlation coefficient
between rankings in the 1890s and the 1900s is 0.96. The correlation of rankings in the
1880s and the 1900s is slightly lower at 0.87. A few countries, like the Netherlands and
Switzerland, saw their operating margins decline by more than other countries.
19 There is large literature analyzing the effects of lower fares and freight charges to the global economy. See O’Rourke and Williamson (1999) and Jacks and Pendakur (2008) for two examples.
13
Portugal had the highest operating margins among all countries. Portugal’s
railroad profits equaled 52% of its revenue in the 1900s. Japan, Spain, India, France, and
Argentina were not far behind Portugal in terms of profits per-unit of revenue. Their
margins ranged from 43% to 49% in the 1900s. Australia, Belgium, Hungary,
Switzerland, Britain, Germany, the U.S., and Russia were in a third group with operating
margins ranging between 31% and 36%. Austria, Sweden, Canada, Norway, and Italy
were in a fourth group with operating margins ranging from 22% to 30%. Lastly the
Netherlands and Denmark had the lowest operating margins. Their profit per unit of
revenue had fallen to less than 16% by the 1900s.
Across countries operating margins declined. The average operating margin
weighted but the sum of ton miles and passenger miles was 38% in the 1880s. By the
1900s it was 34% representing a 11% decline. The U.S. experienced a smaller decline in
operating margins, moderating the world-wide average. Without the U.S. average
operating margins decreased by 21% from the 1880s to the 1900s.
How did rates of return compare? Table 5 addresses this question by listing rates
of return averaged over the 1880s, 1890s, and 1900s. Rates of return were less stable
than operating margins. The correlation of the rankings in the 1890s and 1900s is 0.71.
In the U.S. the rate of return increased from 3.3% to 4.7% from the 1890s to the 1900s.
In Sweden the rate of return decreased from 4% to 3.1%. The rate of return was highly
correlated with operating margins. The correlation between the two rankings in the
1900s was 0.72. High operating margins generally implied higher rates of return.
Japan had the highest rate of return of all countries at 7.9% in the 1900s. Belgium,
Germany, and India were a second group with rates of return ranging between 5.1 and
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5.8% in the 1900s. The U.S., Argentina, Spain, Switzerland, and France were in the
middle tier with rates of return between 4.1 and 4.7%. Russia, Australia, Hungary,
Sweden, Austria, and Britain all had rates of return in the 3% range. Italy, Canada, and
Norway had the lowest rates of return, averaging less than 3% in the 1900s.
Across countries rates of return were stable from the 1880s to the 1890s but then
increased in the 1900s. The average rate of return weighted by ton miles plus passenger
miles increased from 3.8% in the 1890s to 4.8% in the 1900s. Much of the increase was
driven by the U.S. which experienced a large increase in its rate of return. If the U.S. is
dropped then the average rate of return across countries remained fairly constant.
4. Cost Inefficiency
This section presents estimates of cost inefficiency for railroad systems in each
country. Cost inefficiency occurs when firms or organizations do not minimize costs
with respect to a given output level and input price vector. Inefficiency is usually
measured at the level of a firm or organization, but in this case it is measured at the
country level.20 In order for aggregation to make sense there needs to be a common
aggregate production function for railroad services in each country. Differences in total
costs would then reflect differences in output levels, input prices, and the efficiency of
the representative railroad.21 The assumption of a common production function is not
unreasonable in this setting. Steam-driven locomotives were dominant in the industry
20 There is a literature which analyzes stochastic frontier models at the industry country-level. See Afonso and St. Aubyn (2005) or Greene (2004) for two examples. 21 The interpretation of scale economies is somewhat different in the aggregate country-level analysis as opposed to the firm-level. If the estimates show that a 1% increase in ton miles or passenger miles results in a less than 1% increase in costs, then this would be evidence for industry-level economies of scale. Such cases are usually indicative of externalities. As railroad firms increase their output then by some channel, like technological change or regulatory influence, they reduce the costs of other railroads as well.
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before World War I. Moreover most countries imported locomotives, iron, and steel
from a small number of countries like Britain, Germany, France, or the United States.22
There are several methods for estimating cost inefficiency using panel data. One
of the most commonly-used is the ‘true fixed effects’ model. The baseline model is
usually specified as a Cobb-Douglass cost function taking the following form:
ititi
J
j
jii
jK
k
kii
kit vupqc ++++= ∑∑
==
αγβ11
lnlnln (1)
where itcln is the natural log of real costs for unit i in year t, kitqln is the log of output k
for unit i in year t, jitpln is the log of the real price of input j for unit i in year t, iα is a
fixed effect for unit i, itu is a half-normal random variable with mean 0 and variance 2uσ ,
and itv is a normal random variable with mean 0 and variance 2vσ . The term itu
measures the cost inefficiency of unit i in year t and the second term itv is the error term.
Cost inefficiency is estimated for each unit and year using the composite error term
ititit vu +=ε and the parameter estimates for 2uσ and 2
vσ . The conditional mean for cost
inefficiency of unit i and year t is calculated using the following formula from Jondrow
et. al. (1982):
⎥⎦
⎤⎢⎣
⎡−
Φ−+= z
zzuE itit )(1)(
1]|[ 2
φλ
σλε (2)
where φ is the p.d.f of the standard normal, Φ is the c.d.f. of the standard normal,
22vu σσσ += ,
v
u
σσλ = ,
σλε itz = , and ititit vu +=ε .
22 The main technological differences were the gauges of railroad track. Some countries primarily used the standard gauge width of 4 ft. 8.5 inches, but others operated with broader gauges exceeding 5 ft. Gauges might have affected costs, but their influence was likely to be fixed over the period from 1883 to 1912. The subsequent analysis addresses such issues by including country-fixed effects.
16
The true fixed effects model is preferred in this setting because it has several
advantages over the fixed effects model and the pooled normal half-normal model.23
First, unlike the fixed effects model, it allows cost inefficiency to be time-varying. This
is an important property in this context because there is no reason to believe that the
efficiency of railroads remained constant over a thirty-year period. Second, unlike the
pooled normal half-normal model, the true fixed effects model includes a unit fixed effect
which captures all time-invariant unobservable factors. Unobserved factors, like
geography, should be separated from inefficiency because it will cause some units to
operate at higher or lower costs because of factors beyond their control.24
There are three more specification issues that need to be addressed before
reporting the estimates. First, the data set on railroad expenses and outputs spans 30
years for some countries. As a result there are likely to be time-varying unobservable
factors which influence costs besides output, input prices, and inefficiency. The main
type of time-varying unobservable factor would be technological change. A quadratic
time trend is included because it allows the cost frontier to evolve over time in flexible
manner without sacrificing too many degrees of freedom.25
Second, most inefficiency studies add control variables. In the case of railroads
several scholars have added a variable for train miles divided by total track miles. This
measure was not used here because it was not available for many countries. Instead the
23 The fixed effects effect model does not include a half-normal random variable itu and instead it defines inefficiency using the estimated fixed effects from the cost function. The pooled normal half normal includes a half-normal random variable itu but it drops the unit fixed effect. 24 Greene (2005) argues that cost inefficiency is generally over-estimated in the fixed effects model and the pooled normal half-normal models because they conflate unobserved heterogeneity with inefficiency. The true fixed effects model can address this problem as long as there is sufficient within-unit variation. 25 Year fixed-effects provide another way of controlling for technological changes. However inefficiency could not be estimated using year fixed effects because there are few cross-sectional units relative to time-units.
17
log of railroad miles per square mile of land area and the log of population density were
added. The log of train miles divided by railroad miles was always insignificant when it
was included in specifications with the log of railroad miles per square mile and the log
of population density.26 The same is true for the average haul.27
Third, theory requires that cost functions must be homogenous of degree one in
prices. In the Cobb-Douglass specification this implies that the coefficients on the price
variables sum to one. Imposing this restriction implies that the Cobb-Douglass stochastic
cost function for railroads can be transformed to the following:
itititlaborit
capitcap
laborit
coalitcoal
ititilaborit
it vuxpp
pppmtm
pc +++⎟⎟
⎠
⎞⎜⎜⎝
⎛+⎟⎟
⎠
⎞⎜⎜⎝
⎛+++=⎟⎟
⎠
⎞⎜⎜⎝
⎛ηγγββα lnlnlnlnln 21 (3)
The left hand side variable is the log of real total expenses divided by real wages and on
the right-hand side there is a country fixed effect iα , variables for the log of ton miles
and passenger miles, a variable for the log of real coal prices divided by real wages, a
variable for the log of real capital prices divided by real wages, the control variables itx ,
a variable for inefficiency itu , and lastly the error term itv . Once the parameters are
estimated the coefficient laborγ can be recovered by subtracting )( capcoal γγ + from one.
4.1 Inefficiency Estimates
Table 6 reports summary statistics for the key variables. The bottom panel reports
the standard deviation across all observations. It also reports the average of the standard
26 All three of these variables are highly correlated. The correlation between the log of train miles over total miles and the log of railroad miles per square mile is 0.73. The correlation between the log of train miles over total miles and the log of population density is 0.73. 27 The average trip length had a negative and significant effect on costs even after including railroad miles per square mile and population density. Adding the average trip length does not affect the inefficiency estimates discussed later.
18
deviation within countries. Most of the variation in real total expenses, output, wages,
capital prices, and coal prices is between countries, but as the figures show there is also
significant variation within countries. For most variables around 20% is due to within-
country variation. This implies there is some variation to separate inefficiency from
unobserved heterogeneity using country fixed effects.
Table 7 reports the parameter estimates for the stochastic cost function.28 In
column (1) the estimates show there were moderate economies of scale at the industry-
level. The estimate for the degree of economies of scale is 1.13.29 In column (2) the
degree of scale economies is more difficult to quantify because it includes proxies for
density. The log of railroad miles per square mile and population density are highly
significant and absorb much of the impact of passenger miles and ton miles.
The time trend coefficients (i.e. the year and the year square) are also highly
significant in the cost function. Over the period from 1885 to 1912 the time trend
coefficients account for a 21% decrease in costs, falling more rapidly in the 1880s and
1890s than the 1900s. The most plausible explanation is that railroad costs throughout
the world were driven down by technological change. Interpreted in this way, the results
imply that technological change reduced costs by an annual rate around 1%.30
The cost function estimates also imply that the share of capital costs is 60%, the
share of fuel costs is 19%, and the share of labor costs are 21%. There is evidence from
Australia which suggests that these proportions are not unreasonable. In Australia
28 The cost function was estimated using maximum likelihood procedures in the software program LIMDEP. 29 Economies of scale equal 1/(0.54+0.35)=1.13 30 The annual rate of 1% is based on a 21% decline in costs over a 27 year period. A 1% elasticity of costs with respect to time is fairly similar to what Herranz-Locan (2006, p. 862) found for Spanish railways from 1891 to 1913.
19
working expenses were divided into four categories: (1) maintenance of the track and
stations, (2) maintenance to locomotives, carriages, and wagons, (3) traffic expenses, and
(4) other charges. In 1909 maintenance to track and stations was 25%, maintenance to
locomotives, carriages, and wagons was 44%, traffic charges were 26%, and other
charges were 5%.31 Traffic charges largely represent fuel expenses, which are estimated
to be 19% of total expenses in the model. Maintenance to the way and maintenance to
the rolling stock account for 69% of the costs in Australia. It is not unlikely that the
majority of these expenditures went to materials and replacement parts which are
estimated to be 60% of total expenses in the model.
The main findings are the cross-country estimates of cost inefficiency. The
inefficiency estimates are calculated using the formula for the conditional mean of the
inefficiency term in equation (2). The estimates are based on the specification in column
2 of table 7 which includes railroad miles per square mile and population density. The
top panel of table 8 reports the average inefficiency by country in the 1880s, 1890s, and
1900s. It also lists the rankings from the least cost inefficient (i.e. most efficient) to the
most cost inefficient. The average cost inefficiency is 0.042. This would imply that the
average country could have reduced its operating costs by 4.2% if it eliminated all
inefficiency. Put differently, it could have raised its operating margins by around 7%.
There is little correlation between the rankings of cost inefficiency in the 1890s
and 1900s. Between the 1880s and 1900s the correlation increases but it is negative
rather than positive. Countries which were more inefficient in the 1880s became less so
later; the opposite was true for countries that were less inefficient in the 1880s.
31 See Yearbook of the Commonwealth of Australia (1910, p. 721) for more details.
20
France, Spain, and the U.S. all had the lowest cost inefficiency in the 1900s. In
France, cost inefficiency decreased from the 1880s to the 1900s. In Spain cost
inefficiency was high in the 1880s and then decreased in the 1890s and 1900s. The
pattern was different in the U.S, where inefficiency rose in the 1880s and early 1890s and
then declined in the late 1890s and 1900s. Germany, Russia, and Australia were in a
second group with inefficiency levels around 0.04 in the 1900s. Cost inefficiency
increased in Germany from the 1880s to the 1890s, but then decreased in the 1900s,
especially after 1903. Norway and Austria had slightly higher cost inefficiencies at
around 0.045 in the 1900s. In Austria, inefficiency rose significantly from 1907 onwards.
Japan, Britain, Switzerland, and Sweden were in the next group with cost inefficiencies
around 0.05 in 1900s followed by Argentina, Canada, and India with cost inefficiencies
above 0.05 in the 1900s. Cost inefficiency increased significantly in all these countries
from the 1890s to the 1900s.
Across countries cost inefficiency declined, particularly in the 1900s. The
average inefficiency weighted by ton miles plus passenger miles was 0.043 and 0.045 in
the 1880s and 1890s, but then it fell to 0.038 in the 1900s. The U.S. dominates the trend
because of its large size. Without the U.S. average inefficiency rose from 0.035 to 0.04
to 0.042. The trend in U.S. inefficiency was different from most other countries as it was
with fares, freight charges, operating margins, and rates of return.
The cost inefficiency estimates are robust to different specifications. None of the
results from other models are reported, but they can be quickly summarized. Estimated
inefficiency is nearly identical when the assumption that input prices are homogeneous of
degree one is not imposed. The correlation coefficient with the baseline estimates is
21
0.99. If a translog specification is used the mean inefficiency is larger (0.052 vs. 0.042),
but the estimates of inefficiency remain highly correlated with the baseline model
(ρ=0.89). As a result, the rankings of countries are mostly unaffected by the choice of the
Cobb-Douglass or the translog cost function. The one specification where the
inefficiency estimates do change is when the country fixed effects are dropped. In this
case mean inefficiency rises dramatically to 0.311 and the correlation with the baseline
true fixed effects model is only 0.19. This is not surprising since the country fixed effects
capture many unobservable factors that would be incorrectly attributed to inefficiency.
It is instructive to briefly examine cost inefficiency in France, the U.S. and
Britain. Figure 1 shows the cost inefficiency estimates for these three advanced industrial
economies from 1883 to 1912. The trends are consistent with literature that emphasizes
the rising productivity of U.S. railroads and the declining productivity of British
railroads.32 The estimates also suggest there were particular years when the level
inefficiency changed. In some cases the dates correspond with changes in policy. In
France, an 1883 law made it easier for private railroad companies to obtain assistance
from the state if they experienced financial distress. Doukas (1945, p. 49) argues that the
law ushered in a period of sustained prosperity, unparalleled in the annals of French
railroads. This characterization is consistent with the estimates that cost inefficiency
decreased in France from 1885 and reached its lowest level in 1906. There was another
major policy change in France in 1906-07 when a bill was introduced to nationalize one
of the largest private railroad companies—the Western (Doukas 1945, p. 56). Around the
32 See, for example, Crafts, Mills, and Mulatu (2007) and Crafts, Leunig, and Mulatu (2008) who argue there was a managerial failure in British railways. Raper (1912) and Fishlow (1966) also emphasize the greater performance of U.S. railroads at the turn of the century. For the larger context see Broadberry’s (2006) analysis of U.S. and British labor productivity in services.
22
time of the nationalization inefficiency increased sharply in France, suggesting there was
a connection with the state takeover of the Western.
In the U.S. the Inter-State Commerce Act was passed in 1887. Its goal was to
eliminate collusion and price discrimination among railroads in the U.S. by creating a
new regulatory authority, the Inter-State Commerce Commission (ICC). In the wake of
the law inefficiency for U.S. railroads rose sharply, suggesting that its initial effects were
to raise inefficiency. After 1892 cost inefficiency declined dramatically in the U.S.,
suggesting that the long-run effects of the Inter-state commerce act may have been
positive. In Great Britain it is less clear what might have triggered the rise in inefficiency
in the early 1900s. There was a major court case in 1899 which reinterpreted the Railway
and Canal Traffic Act of 1894 and stated that freight rates would not rise with inflation.33
Of course, other factors may have been at work as well in Britain after 1900.
5. The Effects of Cost Inefficiency on fees and profits
Fees and profits are determined by a variety of factors including inefficiency
changes, technological changes, and increases in scale.34 This final section investigates
whether changes in inefficiency had a large effect on fees and profits. Equation (4)
shows a linear relationship between real fares, real freight charges, operating margins, or
rates of return ( )ity in country i and year t and a set of variables.
ititiitit tcyinefficieny εαδαβ +⋅+++⋅= (4)
33 See Crafts, Leunig, and Mulatu (2008) p. 12 for more details. 34 Inefficiency is distinct from technological change which can be interpreted as a shift in the frontier cost curve or the realization of economies of scale which can be interpreted as a movement along the frontier cost curve.
23
itcyinefficien is the inefficiency estimate for country i in year t discussed in the previous
section, iα is a country-fixed effect, tδ is a year fixed effect, ti ⋅α is a country-specific
time trend, and itε is the error term. The country-fixed effect, year fixed effect, and
country specific time trends control for unobservable factors, including geography and
technological changes. The main variable of interest is inefficiency.
The top of table 9 reports the regression results. Column (1) shows that higher
inefficiency had a positive and significant effect on real fares. The results are similar for
freight charges, except the coefficient is not statistically significant (see column 2). The
results in columns (3) and (4) show that higher inefficiency had a negative and significant
effect on operating margins and rates of return. On the whole the findings indicate that
greater efficiency contributed to lower fees and higher profits.
The bottom of table 9 illustrates the magnitudes of the estimates. The first row
shows the effect of a one standard deviation increase in inefficiency on fares, freight
charges, operating margins, or rates of return.35 The second row divides the estimated
effect by the standard deviation of fares, freight charges, operating margins, or rates of
return to measure the relative importance of inefficiency in explaining the variation in
fees and profits. In all cases the standard deviation is the average of the standard
deviation within countries. The counter-factual is what would have happened to fees and
profits within countries if their inefficiency increased.
The results show that inefficiency had a greater effect on profits than fees. A one-
standard deviation increase in inefficiency lowered operating margins by 0.43 of a
standard deviation and lowered rates of return by 0.17 of a standard deviation. A one- 35 Recall that fares and freight charges are measured in 1905 pence per ton miles or passenger mile. Operating margins and rates of return are in decimal units (i.e. 0.35 or 0.04).
24
standard deviation increase in inefficiency lowered fares by 0.14 of a standard deviation
and freight charges by 0.07 of a standard deviation. Inefficiency mattered, but it was
clearly not the most important determinant of fees and profits. This result makes sense
considering the cost function estimates. Recall that if the average country eliminated all
its inefficiency then costs would fall by 4.2% and operating margins and rates of return
would rise by 7%. For comparison consider the effects of technological change proxied
by the time trend coefficients. They imply that technological change decreased railroad
costs by 21% from 1885 to 1912, which would increase operating margins and rates of
return by 35%. The effects of scale are more difficult to quantify. In the simplest cost
function model the estimates suggest that a 1% increase in ton miles and passenger miles
would raise costs by only 0.88% (see column 1 in table 7). This implies that a 10%
increase in scale would roughly correspond to a 9% decrease in per-unit costs, which
would raise operating margins and rates of return by 15%. Fixed country characteristics,
like geography, were also quite important in determining fees and profits. If country-
fixed effects are dropped from the fees and profits regressions, then the R-square
decreases substantially. This implies that some countries were destined to have relatively
high fees and low profits, irrespective of changes in inefficiency, technology, and scale.
6. Conclusion
This paper sheds light on the comparative performance of a leading sector in the
world economy at the turn of the twentieth century. It estimates fees, profits, and cost
inefficiency across a number of countries from 1880 to 1912. It shows that fees declined
for most countries, while profits were more stable. Cost inefficiency varied significantly
25
across countries and over time. The paper also investigates the effects of cost
inefficiency on fees and profits. The results show that lower inefficiency increased
profits more than it lowered fees, but its overall effect was relatively small. One main
conclusion therefore is that inefficiency accounts for less of the variation in fees and
profits than factors like technological change and greater scale.
As a final remark, inefficiency, technological change, and greater scale were all
important determinants of railroad performance, but they might be regarded as proximate
factors only. More fundamental factors might include geography, economic structure,
and institutions. In terms of the latter there was an increase in state ownership and
regulation from 1870 to 1912.36 These changes in the ‘rules-of-the-game’ likely affected
the rate of innovation, the adoption of new technologies, and the organization of services.
Future research will explore how institutional factors affected railroad performance and
through this channel how they affected the world economy at the turn of the century.
7. Appendix
Table 10 lists real fares per passenger mile by country and year beginning with the
earliest estimates. Fares are reported in 1905 British pence. Table 11 lists real freight
charges per ton mile by country and year in 1905 British pence. Table 12 lists operating
margins (in percentages) by country and year. Table 13 lists rates of return (in
percentages) by country and year. Lastly table 14 lists cost inefficiency by country and
year.
36 For a summary of changes in state ownership and regulation affecting railroads see Millward (2004) and Bogart (forthcoming).
26
References
Afonso, Antonio and Miguel St. Aubyn. “Non-Parametric Approaches to Education and
Health Efficiency in OECD Countries.” Journal of Applied Econometrics 8 (Nov.
2005), pp. 227-246.
Arnold A. and S. McCartney. “Rates of return, concentration levels and strategic change
in the British railway industry 1830-1913.” Journal of Transport History 25
(2005), pp. 41-60.
Bogart, Dan. “Nationalizations and the Development of Transport Systems: Cross-
Country Evidence from Railroad Networks: 1860-1912.” Forthcoming Journal of
Economic History.
British Board of Trade, The Statistical Abstract for the Principal and Other Foreign
Countries, Various Years.
British Board of Trade, The Statistical Abstract for the Several Colonial and other
Possessions of the United Kingdom, Various Years.
British Board of Trade, The Statistical Abstract for the United Kingdom, Various Years.
British Board of Trade, Returns Relating to the Production and Consumption of Coal,
Various Years.
Broadberry, Stephen. Market Services and the Productivity Race, 1850-2000. Cambridge:
Cambridge University Press, 2006.
Cianci, Ernesto. Annali di Statistica serie 6 Vol. 20 Rome: ISTAT, 1933.
Collins, William J. and Jeffrey G. Williamson. Capital-Goods Prices and Investment,
1870-1950. Journal of Economic History 61 (March 2001): 59-94.
27
Crafts, Nicholas, Terence C. Mills, and Abay Mulatu. “Total factor productivity growth
on Britain’s railways, 1852–1912: A reappraisal of the evidence,” Explorations in
Economic History 4 (Oct 2007): 608-634.
Crafts, Nicholas, Timothy Leunig, and Abay Mulatu. “Were British Railway Companies
Well Managed in the early twentieth Century?,” Forthcoming, Economic History
Review.
Farsi, Mehdi, Massimo Filippini, and William Greene. “Efficiency Measurement in
Network Industries: Application to Swiss Railway Companies,” Journal of
Regulatory Economics 20 (2005): 69-90.
Fishlow, Albert. “Productivity and Technological Change in the railroad sector, 1840
1910.” In Brady, Dorothy (ed.) Output, Employment, and Productivity in the
United States after 1800. New York: NBER, 1966.
Foreman-Peck, James. “Natural Monopoly and Railway Policy in the Nineteenth
Century.” Oxford Economic Papers 39 (1987): 699-718.
Greene, William. “Distinguishing between Heterogeneity and Inefficiency: Stochastic
Frontier Analysis of the World Health Organization’s Panel Data on National
Health Care Systems,” Health Economics 13 (2004): 959-980.
Greene, William. “Reconsidering Heterogeneity in Panel Data Estimation of the
Stochastic Frontier Model,” Journal of Econometrics 126 (2005): 269-303.
Gregory, Paul. Russian National Income, 1885-1913. Cambridge: Cambridge University
Press, 1982.
Herranz-Locan, Alfonso. “The Spanish Infrastructure Stock, 1844-1935.” Research in
Economic History 23 (2005): 85-129.
28
Herranz-Locan, Alfonso. “Railroad Impact in Backward Economies: Spain, 1850-1913.”
Journal of Economic History 66 (Dec. 2006): 853-881.
Jacks, David and K. Pendakur. “Global Trade and the Maritime Transport Revolution.”
Forthcoming in the Review of Economics and Statistics.
Jondrow, J., K. Lovell, I. Materov, and P. Schmidt. “On the Estimation of Technical
Inefficiency in the Stochastic Frontier Production Function Model,” Journal of
Econometrics 19 (1982): 233-238.
Kumbhaker, Subal and C.A. Knox Lovell. Stochastic Frontier Analysis. Cambridge:
Cambridge University Press, 2000.
Leunig, Tim Leunig, Timothy. "Time is Money: A Re-Assessment of the Passenger
Social Savings from Victorian British Railways," The Journal of Economic
History 66 (Sept. 2006): 653-673.
MacKinnon, Mary and Chris Minns. “The Costs of Doing Hard Time: A Penitentiary
based Regional Price Index for Canada, 1883-1923,” The Canadian Journal of
Economics 40 (May 2007): 528-560.
MacKinnon, Mary. “New Evidence on Canadian Wage Rates, 1900-1930,” The
Canadian Journal of Economics 29 (Feb. 1996): 114-131.
Mesch, Michael. Arbeiterexistenz in der Spatgrunderzeit. Europaverlag Wien, 1984.
Millward, Robert. Private and Public Enterprise in Europe. Cambridge: Cambridge
University Press, 2004.
Minster for Home Affairs, Australia. Year Book of the Commonwealth of Australia,
1910
29
Mitchell, B.R. British Historical Statistics. New York: Cambridge University Press,
1988.
Mitchell, B.R. International Historical Statistics: Africa, Asia, & Oceania, 1750-1988.
New York: MacMillan, 1995.
Mitchell, B.R. International Historical Statistics: The Americas, 1750-2000. New York:
Palgrave MacMillan, 2003.
Mitchell, B.R. International Historical Statistics: Europe, 1750-1988. New York,
MacMillan, 1992.
Mulhall, Michael. Dictionary of Statistics. London: Routledge, 1892.
O’Rourke, Kevin and Jeffrey Williamson. Globalization and History: The Evolution of a
Nineteenth Century Atlantic Economy. Cambridge: M.I.T. Press, 1999.
Raper, Charles Lee. Railway Transportation: a History of its Economics and its relation
to the State. New York, Knickerbocker Press, 1912.
Republic of Argentina, Ministerio de Obras Publicas. Estadistica de Los Ferrocarriles en
Explotacion, Various Years.
Siegenthaler, Hansjoerg. Historische Statistik der Schweiz Chronos, Zuerich, 1996.
Strong, Josiah (ed.). Social Progress. New York, Baker and Taylor, 1904.
Studer, Roman. “When Did the Swiss Get so Rich? Comparing Living Standards in
Switzerland and Europe,” Forthcoming Journal of European Economic History.
U.S. Department of Commerce. Statistical Abstract of the United States, various years.
Van Riel, Arthur. “Prices of consumer and producer goods, 1800-1913.” Available at
http://iisg.nl/hpw/brannex.php.
30
Williamson Jeffrey G. “The Evolution of Global Labor Markets since 1830: Background
Evidence and Hypotheses,” Explorations in Economic History 32 (April 1995):
141-196.
Table 1: Working to Total Expenses for Selected Countries Country Average ratio of working to total expenses Norway 0.952 Sweden 0.944 Belgium 0.984 France 0.974 Italy 0.914 Japan 0.95 Germany 0.972 India 0.972 Austria-Hungary 0.835 Algeria-Tunis 0.955 Overall Average 0.941 N 182
Sources: see text.
Table 2: Average Real Fares per Passenger Mile Across Countries, 1880-1910
Fare
Rank
country
1880-89
1890-99
1900-09
1880-89
1890-99
1900-09
India 0.23 0.25 0.2 1 1 1 Russia 0.42 0.35 0.3 2 2 2 belgium 0.47 0.43 0.34 3 4 3 japan 0.36 0.35 3 4 hungary 0.5 0.48 6 5 austria 0.5 0.49 5 6 germany 0.78 0.65 0.51 6 7 7 italy 0.7 0.63 9 8 norway 0.81 0.7 0.65 7 8 9 switzerland 0.97 0.81 0.7 10 12 10 netherlands 0.77 0.83 0.71 5 13 11 sweden 0.99 0.91 0.71 11 15 12 australia 0.73 13 france 0.88 0.77 0.76 8 11 14 britain 0.75 0.77 0.79 4 10 15 canada 0.88 0.84 0.86 9 14 16 argentina 1.09 1 16 17 us 1.29 1.19 1 13 17 18 spain 1.24 12 weighted average 0.85 0.72 0.64
Notes: Fares are expressed in 1905 British Pence. 240 pence=1 pound. Sources: see text.
Table 3: Average Real Freight Charges per ton Mile Across Countries, 1880-1910
freight charge
Rank
country
1880-89
1890-99
1900-09
1880-89
1890-99
1900-09
belgium 0.28 0.3 0.3 1 1 1 canada 0.39 0.4 0.36 2 2 2 us 0.6 0.5 0.38 4 3 3 Russia 0.69 0.52 0.42 6 4 4 india 0.58 0.58 0.44 3 5 5 japan 0.69 0.47 8 6 netherlands 0.62 0.62 0.61 5 6 7 hungary 0.64 0.64 7 8 austria 0.73 0.68 9 9 germany 0.91 0.82 0.68 7 10 10 france 0.92 0.85 0.75 8 11 11 sweden 1.18 0.97 0.75 11 12 12 argentina 1.17 0.95 15 13 norway 1.29 1.17 1.03 13 16 14 britain 1.01 1.11 1.08 9 14 15 australia 1.08 16 italy 1.15 1.06 1.23 10 13 17 switzerland 1.28 1.26 1.38 12 17 18 spain 1.69 14 weighted average 0.69 0.6 0.48
Notes: Freight charges are expressed in 1905 British Pence. 240 pence=1 pound. Sources: see text.
Table 4: Average Operating Margins Across Countries, 1880-1910
Operating
margins (in %)
Rank
country
1880-89
1890-99
1900-09
1880-89
1890-99
1900-09
portugal 58.5 51.7 52.1 2 3 1 japan 54.5 49.4 1 2 spain 51.6 52.6 48.7 3 2 3 india 65 48.6 47.4 1 4 4 france 46.8 46.3 45.2 5 5 5 argentina 41.3 43.4 8 6 australia 36.4 7 belgium 43.4 42.2 35.8 8 6 8 hungary 41.5 35.6 7 9 switzerland 47.1 40.7 35.1 4 9 10 britain 44.8 40.3 33.9 6 10 11 germany 44.2 39.6 33.8 7 13 12 us 35.9 31.4 33.6 11 15 13 Russia 34.6 40.1 31.2 12 12 14 austria 39.3 29.5 14 15 sweden 41.9 40.2 25.5 10 11 16 canada 21.7 26.5 24.6 16 17 17 norway 28.4 26.2 24.3 14 18 18 italy 30.7 30.1 22.6 13 16 19 netherlands 43.2 13 15.6 9 19 20 denmark 24 12.5 15 21 weighted average 37.9 34.1 33.8
Notes: Operating margins are profits divided by revenues. Sources: see text.
Table 5: Average Rates of Return Across Countries, 1880-1910
rate of return
(in %)
Rank
country
1880-89
1890-99
1900-09
1880-89
1890-99
1900-09
japan 8 7.9 1 1 belgium 5.8 5.7 2 2 germany 4.7 5.3 5.5 1 3 3 india 4.9 5.1 4 4 us 3.8 3.3 4.7 6 13 5 argentina 2.7 4.4 14 6 spain 4.7 4.6 4.3 2 5 7 switzerland 3.9 4.2 4.2 4 7 8 france 4.2 3.8 4.1 3 11 9 Russia 3.9 3.5 9 10 australia 3.5 11 hungary 4.3 3.3 6 12 sweden 3.4 4 3.1 7 8 13 austria 3.8 3.1 10 14 britain 3.9 3.5 3.1 5 12 15 italy 2.2 16 canada 1.3 1.6 2.1 9 16 17 norway 1.7 1.9 2.1 8 15 18 weighted average 3.9 3.8 4.8
Notes: Rates of return are profits divided by the cost of construction. Sources: see text.
Table 6: Summary Statistics for Stochastic Frontier Analysis Variable mean st. dev. min max log real total expenses 16.23 1.64 12.12 19.81 log passenger miles 21.55 1.55 17.27 24.23 log ton miles 22.04 1.9 16.98 26.26 log real coal prices 2.23 0.48 1.14 3.88 log real wages 2.69 0.83 0.87 4.11 log real capital prices 9.7 0.56 8.7 11.06 log rr miles per square mile -3.23 1.41 -6.84 -1.38 log population density -2.79 1.92 -7.66 -0.5 N 366
standard deviation
overall Within fraction of variation within log real total expenses 1.64 0.38 0.23 log passenger miles 1.55 0.37 0.24 log ton miles 1.9 0.37 0.19 log real coal prices 0.48 0.14 0.29 log real wages 0.83 0.1 0.12 log real capital prices 0.56 0.07 0.13 log rr miles per square mile 1.41 0.17 0.12 log population density 1.92 0.08 0.04
Sources: see text.
Table 7: Cost Function Parameter Estimates (1) (2)
Coefficient Coefficient
Variable (Standard
Error) (Standard
Error) ln ton miles 0.344 0.138 (0.035)* (0.038)* ln passenger miles 0.54 0.414 (0.041)* (0.038)* ln rail per sq. mi 0.52 (0.072)* ln pop density 0.62 (0.094)* ln(real coal prices/wages) 0.136 0.194 (.033)* (.03)* ln(capital prices/wages) 0.648 0.592 (.052)* (.046)* Year -1.067 -0.992 (.269)* (.0237)* year squared 0.000279 0.000259
(.71255D-
04)* (.625796D-
04)* country fixed effects Yes Yes N 366 366 log likelihood 412 452 sigma squared v 0.005 0.004 sigma squared u 0.002 0.003 lambda 0.59 0.835 sigma 0.085 0.081
Notes: * indicates statistical significance at the 10% level and below. The parameters were estimated using maximum likelihood. Sources: see text.
Table 8: Average Cost Inefficiency Estimates Across Countries, 1880-1910
Inefficiency
Rank
country
1880-89
1890-99
1900-09
1880-89
1890-99
1900-09
france 0.048 0.038 0.033 7 7 1 spain 0.064 0.039 0.036 9 10 2 us 0.045 0.047 0.037 6 15 3 germany 0.039 0.045 0.04 4 14 4 russia 0.039 0.04 9 5 australia 0.04 6 norway 0.042 0.043 0.046 5 12 7 austria 0.03 0.047 4 8 japan 0.035 0.049 6 9 britain 0.031 0.034 0.05 2 5 10 switzerland 0.027 0.04 0.05 1 11 11 sweden 0.025 0.051 1 12 argentina 0.029 0.053 2 13 india 0.038 0.055 8 14 canada 0.036 0.03 0.061 3 3 15 belgium 0.049 0.043 8 13 weighted average 0.043 0.045 0.038
Sources: see text.
Table 9: The effects of Cost Inefficiency on Fees and Profits (1) (2) (3) (4)
Fares freight
charges operating margins rates of return
coefficient coefficient coefficient coefficient variable (st. error) (st. error) (st. error) (st. error) inefficiency 0.793 0.466 -1.353 -0.07 (0.194)* (0.297) (.111)* (.019)* country fixed effects yes yes Yes yes year fixed effects yes yes Yes yes Country-specific trends yes yes Yes yes N 346 346 365 365 R-square 0.98 0.97 0.93 0.92 effect from a one standard deviation 0.011 0.007 -0.019 -0.001 increase in inefficiency effect relative to a one 0.138 0.067 -0.428 -0.17 standard deviation of fees or profits
Notes: * indicates statistical significance at the 10% level and below. Sources: see text.
Table 10: Average Real Fares per passenger mile by country and year
year rus nor swe net bel fra swz spa ita austria hun japan us arg Gbr ger ind austral can 1870 0.8 1871 0.64 0.8 0.59 0.77 1872 0.57 0.8 0.49 0.98 0.71 1873 0.54 0.73 0.43 0.97 0.67 1874 0.56 0.78 0.43 0.98 0.68 1875 0.64 0.77 0.44 0.99 0.79 0.71 1876 0.66 0.84 0.4 0.94 0.72 0.66 1877 0.68 0.88 0.4 0.96 0.72 0.67 1878 0.91 0.7 0.41 0.95 0.75 0.68 1879 0.81 0.97 0.74 0.41 0.97 0.78 0.7 0.77 1880 0.79 0.91 0.72 0.49 0.93 0.8 0.71 0.75 1881 0.78 0.92 0.73 0.46 0.93 0.83 0.67 0.77 1882 0.75 0.95 0.74 0.46 0.9 0.91 1.42 0.71 0.77 1883 0.75 0.95 0.74 0.44 0.86 1.01 1.36 0.67 0.81 0.84 1884 0.79 0.96 0.77 0.47 0.86 0.89 1.25 1.32 0.74 0.81 0.88 1885 0.43 0.84 1 0.81 0.5 0.86 1 1.23 1.25 0.77 0.79 0.89 1886 0.42 0.85 1.04 0.84 0.51 0.86 1.03 1.32 1.25 0.82 0.78 0.87 1887 0.41 0.88 1.08 0.81 0.47 0.87 1.29 1.36 1.26 0.8 0.85 0.9 1888 0.87 1.05 0.79 0.47 0.85 0.97 1.2 1.24 0.81 0.69 0.24 0.9 1889 0.83 0.99 0.73 0.46 0.83 0.97 1.1 1.22 0.82 0.69 0.23 0.86 1890 0.83 0.94 0.42 0.84 0.93 1.2 0.82 0.65 0.23 0.85 1891 0.32 0.77 0.89 0.76 0.41 0.83 0.74 1.21 0.77 0.66 0.24 0.87 1892 0.3 0.73 0.92 0.8 0.42 0.77 0.81 1.2 0.76 0.66 0.27 0.88 1893 0.34 0.73 0.94 0.78 0.43 0.76 0.84 0.48 1.16 0.83 0.67 0.26 0.93 1894 0.4 0.73 0.98 0.8 0.45 0.75 0.85 0.5 0.49 0.4 1.17 0.75 0.66 0.31 0.94 1895 0.42 0.69 0.94 0.84 0.44 0.76 0.86 0.51 0.48 0.34 1.21 0.77 0.65 0.28 0.88
1896 0.4 0.66 0.95 0.88 0.45 0.75 0.83 0.54 0.53 0.39 1.19 0.78 0.6 0.25 0.84 1897 0.35 0.66 0.88 0.88 0.43 0.78 0.77 0.71 0.49 0.52 0.35 1.19 0.89 0.75 0.62 0.24 0.76 1898 0.31 0.58 0.86 0.86 0.41 0.76 0.72 0.69 0.46 0.5 0.33 1.17 1.09 0.74 0.6 0.21 0.73 1899 0.31 0.59 0.82 0.84 0.39 0.76 0.72 0.71 0.49 0.5 0.35 1.13 1.27 0.77 0.58 0.21 0.71 1900 0.31 0.81 0.79 0.8 0.35 0.74 0.76 0.71 0.51 0.51 0.36 1.13 1.11 0.79 0.56 0.22 0.73 1901 0.31 0.64 0.8 0.73 0.33 0.75 0.78 0.7 0.51 0.5 0.42 1.11 1.06 0.8 0.56 0.22 0.87 1902 0.36 0.64 0.79 0.75 0.34 0.76 0.8 0.71 0.55 0.52 0.39 1.04 1.05 0.8 0.56 0.21 0.89 1903 0.35 0.64 0.76 0.72 0.33 0.78 0.75 0.7 0.54 0.54 0.36 1.07 1.1 0.79 0.54 0.21 0.89 1904 0.3 0.64 0.75 0.69 0.38 0.78 0.71 0.51 0.51 0.32 1.07 1.17 0.79 0.52 0.2 0.81 0.89 1905 0.32 0.64 0.72 0.7 0.38 0.77 0.68 0.46 0.48 0.3 0.99 0.97 0.8 0.48 0.19 0.75 0.88 1906 0.27 0.62 0.66 0.66 0.33 0.76 0.68 0.55 0.46 0.46 0.31 0.97 0.95 0.78 0.46 0.21 0.73 0.9 1907 0.25 0.62 0.62 0.66 0.32 0.75 0.63 0.54 0.46 0.45 0.29 0.94 0.87 0.77 0.45 0.2 0.75 0.9 1908 0.25 0.62 0.62 0.68 0.31 0.73 0.62 0.54 0.44 0.43 0.37 0.89 0.86 0.78 0.43 0.18 0.68 0.85 1909 0.28 0.61 0.62 0.69 0.31 0.73 0.61 0.58 0.45 0.42 0.33 0.84 0.83 0.78 0.43 0.19 0.67 0.84 1910 0.29 0.59 0.62 0.67 0.31 0.71 0.6 0.6 0.45 0.43 0.3 0.85 0.84 0.76 0.4 0.19 0.65 0.82 1911 0.57 0.62 0.8 0.28 0.68 0.57 0.62 0.4 0.26 0.85 0.83 0.76 0.35 0.2 0.63 0.83 1912 0.54 0.75 0.26 0.54 0.42 0.29 0.79 0.76 0.18 0.61 0.8
Notes: fares are in 1905 British pence per passenger mile
Table 11: Average Real Freight Charges by Country and Year
year rus nor swe net bel fra swz spa ita austria hun japan us arg Gbr ger ind austral can 1870 1.34 1871 1.42 1.27 0.26 0.85 1872 1.24 1.2 0.23 0.91 0.84 1873 1.16 1.18 0.22 0.91 0.83 1874 1.16 1.2 0.24 0.92 0.85 1875 1.21 1.15 0.27 0.96 1.19 0.9 1876 1.14 1.12 0.25 0.93 1.12 0.84 1877 1.17 1.13 0.25 0.92 1.12 0.85 1878 1.25 0.25 0.94 1.18 0.89 1879 1.4 1.33 0.26 0.93 1.19 0.92 1880 1.3 1.24 0.27 0.92 1.17 0.92 0.93 1881 1.28 1.19 0.69 0.27 0.92 1.18 0.87 0.89 1882 1.19 1.19 0.61 0.26 0.92 1.25 0.68 0.91 0.9 1883 1.2 1.13 0.55 0.26 0.88 1.28 0.67 0.87 0.9 0.4 1884 1.24 1.15 0.62 0.29 0.9 1.21 1.58 0.63 0.96 0.95 0.39 1885 0.69 1.29 1.19 0.64 0.3 0.93 1.31 1.55 1.2 0.6 1.07 0.94 0.37 1886 0.7 1.36 1.23 0.66 0.31 0.93 1.45 1.69 1.16 0.59 1.18 0.94 0.36 1887 0.68 1.39 1.21 0.63 0.29 0.92 1.37 1.85 1.1 0.57 1.12 0.93 0.41 1888 1.39 1.15 0.62 0.29 0.89 1.33 1.78 1.18 0.54 1.11 0.91 0.59 0.41 1889 1.3 1.08 0.59 0.28 0.97 1.28 1.69 1.11 0.54 1.12 0.83 0.56 0.39 1890 1.31 1.05 0.28 0.86 1.28 1.04 0.51 1.13 0.86 0.53 0.38 1891 0.45 1.26 1.01 0.6 0.28 0.84 1.21 0.51 1.07 0.81 0.52 0.37 1892 0.42 1.24 1.04 0.6 0.3 0.85 1.26 0.54 1.07 0.83 0.58 0.4 1893 0.49 1.22 1.05 0.6 0.3 0.85 1.31 0.65 0.5 1.17 0.84 0.56 0.42 1894 0.58 1.25 1.09 0.64 0.3 0.83 1.35 0.78 0.63 0.87 0.49 1.04 0.85 0.67 0.43 1895 0.64 1.22 0.97 0.67 0.31 0.83 1.36 0.73 0.65 0.72 0.49 1.13 0.83 0.68 0.43
1896 0.63 1.1 0.94 0.64 0.31 0.82 1.32 0.77 0.69 0.88 0.48 1.14 0.84 0.63 0.4 1897 0.54 1.08 0.91 0.63 0.31 0.88 1.24 1.08 0.7 0.64 0.58 0.47 0.87 1.11 0.77 0.59 0.39 1898 0.48 1 0.83 0.59 0.33 0.85 1.15 1.05 0.71 0.63 0.54 0.44 1.25 1.1 0.79 0.51 0.38 1899 0.47 1.02 0.8 0.61 0.33 0.89 1.15 1.05 0.68 0.63 0.55 0.41 1.39 1.11 0.76 0.5 0.36 1900 0.47 1.42 0.77 0.61 0.28 0.78 1.19 1.3 0.72 0.65 0.47 0.41 1.27 1.13 0.73 0.5 0.36 1901 0.46 1.48 0.79 0.59 0.28 0.77 1.24 1.27 0.74 0.65 0.59 0.41 0.99 1.15 0.72 0.48 0.35 1902 0.49 1.11 0.88 0.6 0.29 0.78 1.48 1.24 0.73 0.67 0.52 0.39 0.96 1.13 0.73 0.46 0.34 1903 0.46 1.12 0.75 0.59 0.29 0.78 1.55 1.22 0.74 0.67 0.49 0.4 1.09 1.11 0.72 0.44 0.35 1904 0.43 0.95 0.75 0.57 0.34 0.77 1.51 0.7 0.67 0.45 0.4 1.09 1.09 0.72 0.42 1.13 0.33 1905 0.43 0.91 0.73 0.62 0.32 0.75 1.45 0.63 0.61 0.44 0.39 0.9 1.08 0.69 0.4 1.11 0.34 1906 0.4 0.86 0.73 0.58 0.29 0.74 1.42 1.14 0.61 0.63 0.43 0.36 0.84 1.04 0.65 0.46 1.14 0.35 1907 0.34 0.89 0.69 0.6 0.28 0.72 1.34 1.22 0.64 0.62 0.43 0.36 0.8 1.02 0.64 0.42 1.11 0.45 1908 0.34 0.87 0.67 0.63 0.29 0.7 1.32 1.2 0.62 0.6 0.41 0.35 0.81 1.05 0.63 0.38 0.99 0.39 1909 0.36 0.67 0.71 0.65 0.29 0.69 1.31 1.23 0.63 0.58 0.44 0.33 0.76 1.03 0.61 0.44 1.02 0.39 1910 0.39 0.84 0.67 0.6 0.29 0.68 1.34 1.18 0.61 0.6 0.41 0.33 0.75 1.01 0.62 0.37 1.09 0.35 1911 0.78 0.65 0.52 0.27 0.64 1.28 1.17 0.57 0.38 0.32 0.77 1.01 0.56 0.37 0.99 0.39 1912 0.73 0.59 0.25 1.23 0.56 0.36 0.71 1 0.54 0.34 0.97 0.37
Notes: Freight Charges are in 1905 British pence per ton mile
Table 12: Operating Margins by Country and By Year
Year rus nor swe net bel fra swz por spa ita aria hun jap us arg gbr ger ind arl can 1870 50 36 50 1871 30 51 49 57 36 18 50 50 1872 34 51 43 50 57 41 28 42 48 44 1873 32 48 32 48 40 35 40 44 39 1874 26 44 34 48 33 32 46 42 37 1875 23 37 36 49 43 33 37 40 43 37 131876 27 39 52 28 49 42 34 37 31 43 38 131877 29 34 49 40 48 40 56 34 36 43 39 121878 34 34 51 42 49 42 55 34 38 44 40 171879 28 19 36 53 41 48 45 54 39 42 45 42 131880 22 24 45 50 41 48 48 56 30 41 46 44 241881 28 26 42 44 39 50 48 59 30 36 45 44 241882 33 33 46 45 40 47 48 55 28 40 45 45 67 181883 36 29 44 44 41 45 48 57 28 36 44 43 66 201884 38 29 43 39 42 44 46 59 54 25 35 44 43 73 181885 40 24 42 43 42 44 46 57 51 29 35 44 42 74 211886 38 27 38 43 44 46 46 61 56 35 36 45 44 74 231887 43 27 37 44 47 47 46 60 37 35 36 45 46 74 241888 30 40 45 49 48 47 59 53 34 31 45 46 46 231889 34 42 35 48 48 48 54 34 32 45 45 46 221890 34 40 19 43 47 45 50 52 32 32 43 38 47 251891 40 30 38 17 44 46 39 47 53 31 42 35 50 231892 36 25 37 4 45 44 37 49 52 30 41 36 50 251893 39 23 37 8 43 43 41 52 51 43 30 40 39 50 251894 42 22 40 7 42 44 42 51 51 44 46 57 30 41 39 50 251895 42 22 40 12 41 46 41 53 50 41 41 58 30 41 43 51 26
1896 42 22 44 13 43 47 40 55 53 42 42 59 31 41 44 49 261897 41 26 46 16 40 48 39 53 29 38 39 56 33 35 40 43 47 291898 40 29 43 17 40 49 41 53 57 30 36 40 53 34 43 38 39 46 311899 38 30 36 16 40 49 43 53 54 29 35 40 44 35 46 37 39 44 311900 36 32 28 11 34 46 41 57 53 25 32 40 50 35 43 35 36 45 281901 33 21 31 12 32 44 37 52 51 23 30 38 52 35 45 33 34 44 271902 35 16 28 17 37 45 39 53 52 25 31 38 49 35 47 34 35 46 271903 36 19 30 17 40 47 36 53 51 28 32 38 49 34 48 34 37 44 261904 35 22 31 17 40 48 34 51 49 32 38 50 32 47 34 37 44 33 211905 32 21 27 16 37 48 35 52 50 33 38 52 33 45 35 37 45 34 201906 26 26 21 17 36 47 35 51 40 22 34 40 52 34 41 34 35 47 38 261907 24 31 19 16 33 44 33 50 40 21 29 34 49 32 39 33 31 48 40 251908 24 29 22 17 34 42 29 51 51 17 24 27 47 30 39 32 26 58 38 231909 31 26 19 17 35 41 32 51 51 21 19 25 44 34 41 34 29 53 37 231910 36 25 25 18 36 40 37 49 17 23 30 46 34 41 35 32 50 35 271911 26 20 35 37 36 16 25 32 49 31 38 35 34 49 34 261912 25 19 33 34 31 51 37 33 32 46 31 27
Notes: Operating Margins are 100*(Revenues- Expenses)/Revenues.
Table 13: Rates of Return by Country and By Year
Year rus nor swe bel fra swz spa ita austria hun jap us arg gbr ger ind austral1870 4.3 2 6.2 1871 2.2 4.8 2.1 4.4 6.9 1872 2.6 4.9 2.6 5.4 4.5 6 1873 3.4 5.1 4.9 2.5 5.1 4.3 5.2 1874 2.8 4.7 4.6 5.2 4.1 4.7 1875 2.5 3.7 4.8 3.9 2.1 4.4 4.2 4.6 1876 3.1 3.4 4.8 3.6 2.1 2.5 4.1 4.4 1877 2.4 2.9 4.5 3.1 2.1 4.2 4.2 1878 2.5 4.7 3.1 2.1 3.9 4 4.2 1879 1 2.4 4.6 3.4 2.5 4.5 3.9 4.2 1880 1.3 3.5 4.9 3.8 2.1 4.7 4.1 4.4 1881 1.5 3.4 5.2 3.9 2.1 4 4.1 4.5 1882 1.9 3.9 4.8 3.6 1.9 4.5 4.1 4.7 1883 1.7 3.8 5.5 4.5 3.9 2 4 4 4.5 1884 1.7 3.6 5.4 3.9 3.7 4.9 1.8 3.5 3.9 4.5 1885 1.3 3.5 5.3 3.7 3.8 4.4 3.5 3.8 4.4 1886 1.6 2.9 5.3 3.6 3.8 5.2 3.7 3.8 4.6 1887 1.5 2.8 6 3.8 4.1 3.5 3.9 3.8 5.1 1888 1.8 3.2 6.5 3.8 4.2 4.9 3.2 3.8 5.3 1889 2.2 3.7 6.5 4 4.6 5 3.3 4 5.5 1890 2.4 3.6 6 3.8 4.4 4.9 3.4 3.8 4.8 1891 3.5 2.1 3.4 6 3.7 3.8 5 3.4 3.7 4.4 4.7 1892 3 1.7 3.3 6 3.5 3.5 4.4 3.3 3.6 4.5 4.9 1893 3.5 1.6 3.3 5.8 3.4 4 4.4 4.6 3.3 3.3 5 5 1894 4 1.5 3.6 5.9 3.6 4.2 4.5 4.1 4.9 7.5 2.9 3.5 4.9 5.8 1895 4.2 1.5 3.9 5.7 3.7 4.2 4.1 3.9 4.3 7.8 2.9 3.5 5.7 5.8
1896 4.1 1.5 4.5 6 3.9 4.3 4.9 4.1 4.7 9.7 3.1 3.6 6.1 4.9 1897 4.2 1.9 5.1 5.7 4 4.3 3.8 3.9 8.7 3.2 1.7 3.4 6.1 4.3 1898 4.2 2.1 5 4.8 4.2 4.6 4.6 3.6 4 7.9 3.7 2.7 3.2 6 4.1 1899 4 2.5 4.4 4.9 4.3 4.9 4.4 3.5 3.9 6.2 3.9 3.6 3.3 6.1 4.2 1900 4 2.9 3.5 4.1 4.2 4.7 4.3 3.2 3.4 7.4 4.4 3.2 3.1 5.8 4.3 1901 3.6 1.8 3.7 3.8 3.8 4.2 4.1 2.9 3.1 7.9 4.6 3.7 2.9 5 4.3 1902 3.9 1.4 3.3 4.3 3.9 4.4 4.6 3.1 3.2 7.5 4.8 3.6 3.1 5.3 4.5 1903 4.1 1.4 3.5 4.1 4.1 4.4 3.2 3.2 7.3 4.8 4.5 3.1 5.8 4.7 1904 3.8 1.7 3.7 4.2 3.9 4.2 3.2 3.4 7.3 4.6 5 3.1 5.9 4.9 2.81905 3.5 1.6 3.2 4.3 4.2 4.2 3.5 3.5 7.8 4.7 5.1 3.1 6.2 5.2 31906 3.1 2.1 2.6 4.4 4.5 3.6 3.6 4.1 8.9 5 5 3.1 6.2 5.6 3.61907 2.8 2.6 2.4 4.2 4.2 3.6 3.3 3.6 9.1 5 4.4 3.1 5.4 5.8 4.11908 2.8 2.6 2.8 4 3.6 4.9 2.7 2.8 8.3 4.2 4.6 2.9 4.4 6.3 3.91909 3.8 2.6 2.3 3.9 3.9 4.9 2.1 2.7 7.3 4.5 5.2 3.1 5 5.8 3.81910 4.6 2.1 3.4 3.9 5.1 2.8 3.3 7.3 4.8 5 3.2 5.6 5.9 3.81911 2.2 3.7 5.2 3.2 3.8 7.8 4.3 3.9 3.3 6.3 6.1 41912 2.2 4.9 3.8 8.5 4.3 3.2 6.1 6.1 3.7
Notes: Rates of return are 100*(Revenues-Expenses)/Construction Costs.
Table 13: Inefficiency by Country and By Year
year rus nor swe bel fra swz spa jap us arg gbr ger ind can austria australia1883 0.03 0.064 0.061 0.06 0.029 0.04 1884 0.034 0.06 0.057 0.024 0.036 0.05 0.031 0.041 1885 0.051 0.054 0.052 0.026 0.049 0.035 0.032 0.038 1886 0.047 0.049 0.041 0.029 0.035 0.034 0.033 0.033 0.026 1887 0.054 0.041 0.042 0.028 0.176 0.041 0.033 0.032 0.037 1888 0.049 0.034 0.041 0.027 0.045 0.047 0.031 0.045 0.046 1889 0.044 0.038 0.039 0.025 0.043 0.049 0.03 0.04 0.035 1890 0.042 0.035 0.038 0.03 0.049 0.05 0.029 0.045 0.035 1891 0.033 0.052 0.035 0.038 0.034 0.05 0.052 0.031 0.057 0.019 0.039 1892 0.044 0.058 0.038 0.042 0.047 0.047 0.066 0.033 0.054 0.028 0.04 1893 0.043 0.056 0.05 0.044 0.04 0.035 0.05 0.035 0.049 0.035 0.041 1894 0.043 0.056 0.053 0.043 0.04 0.043 0.038 0.031 0.047 0.063 0.031 0.023 1895 0.04 0.047 0.058 0.039 0.044 0.035 0.029 0.046 0.035 0.034 0.052 0.023 0.027 1896 0.036 0.041 0.021 0.048 0.035 0.047 0.046 0.039 0.046 0.037 0.031 0.031 0.025 0.03 1897 0.036 0.029 0.018 0.056 0.036 0.048 0.034 0.027 0.04 0.022 0.036 0.031 0.032 0.02 0.033 1898 0.035 0.023 0.023 0.031 0.034 0.039 0.026 0.028 0.047 0.026 0.037 0.051 0.033 0.023 0.033 1899 0.037 0.023 0.037 0.024 0.033 0.032 0.03 0.054 0.039 0.041 0.038 0.051 0.052 0.022 0.033 1900 0.027 0.043 0.059 0.024 0.034 0.032 0.026 0.044 0.042 0.038 0.036 0.049 0.058 0.029 0.039 1901 0.042 0.043 0.042 0.025 0.035 0.041 0.029 0.063 0.04 0.032 0.048 0.045 0.069 0.05 0.039 1902 0.048 0.066 0.053 0.022 0.031 0.051 0.035 0.068 0.037 0.026 0.05 0.042 0.054 0.051 0.04 1903 0.043 0.053 0.04 0.03 0.059 0.032 0.057 0.04 0.046 0.054 0.037 0.074 0.073 0.038 1904 0.031 0.042 0.034 0.027 0.06 0.033 0.041 0.047 0.07 0.055 0.037 0.079 0.082 0.041 0.0481905 0.036 0.043 0.048 0.028 0.055 0.031 0.027 0.037 0.071 0.058 0.037 0.079 0.083 0.035 0.0541906 0.045 0.03 0.072 0.03 0.057 0.07 0.032 0.037 0.079 0.051 0.038 0.059 0.068 0.031 0.0441907 0.05 0.022 0.066 0.037 0.05 0.063 0.062 0.04 0.051 0.049 0.043 0.039 0.082 0.045 0.0361908 0.031 0.044 0.038 0.056 0.022 0.052 0.029 0.061 0.047 0.041 0.016 0.055 0.084 0.031
1909 0.084 0.051 0.041 0.04 0.024 0.044 0.019 0.056 0.047 0.032 0.018 0.041 0.079 0.031910 0.03 0.04 0.049 0.035 0.031 0.019 0.035 0.046 0.028 0.025 0.038 0.069 0.0341911 0.027 0.061 0.038 0.02 0.019 0.022 0.049 0.024 0.031 0.039 0.0431912 0.023 0.047 0.026 0.051 0.026 0.049 0.043 0.038
Notes: Cost Inefficiency is estimated using a stochastic frontier model. 0=no inefficiency.
Figure 1: Cost Inefficiency in Britain, the U.S., and France, 1883-1911
0
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