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NBER WORKING PAPER SERIES

A NEW MONTHLY INDEX OF INDUSTRIAL PRODUCTION, 1884-1940

Jeffrey A. Miron
Christina D. Romer

Working Paper No. 3172

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
November 1989

We are grateful to Todd Clark and David Bowman for excellent research
assistance and to David Romer for helpful comments and suggestions. Financial
support was provided by the NBER, the John M. Olin Fellowship at the NBER
(awarded to Miron), the National Science Foundation (grant SES-8710140 awarded
to Miron and grant SES-8896257 awarded to Romer), and the Institute of Business
and Economic Research at the University of California, Berkeley, This paper is
part of NBER's research programs in Economic Fluctuations and Financial Markets
and Monetary Economics. Any opinions expressed are those of the authors not
those of the National Bureau of Economic Research.

NBER Working Paper #3172
November 1989

A NEW MONTHLY INDEX OF INDUSTRIAL PRODUCTION, 1884-1940

ABSTRACT
The paper derives a new monthly index of industrial production for the
United States for 1884-1940.

This index improves upon existing measures of

industrial production by excluding indirect proxies of industrial activity, by
only using component series that are consistent over time, and by not making
ad hoc adjustments to the data.

Analysis of the new index shows that it has

more within-year volatility than conventional indexes, has relatively
unimportant seasonal fluctuations, and has cyclical turning points that are
grossly similar to but subtly different from existing series.

Jeffrey A.
Department
University
Ann Arbor,

Miron
of Economics
of Michigan
MI 48109

Christina D. Romer
Department of Economics
University of California
Berkeley, CA 94720

In recent years there has been renewed interest in the historical
behavior of the U.S. macroeconomy.

This interest reflects both a desire to

use historical episodes as laboratories in which to test economic theories and
a desire to understand how, and indeed if, the behavior of the U.S. economy
has changed over time.

To give a few examples of recent historical analyses,

De Long and Summers (1986), Miron (1989), and Romer (1986a, 1986b, 1987, 1989)
provide comparisons of output variability over time; Gordon (1982) estimates
the relationship between price changes and output changes in the economy since
1890; Calomiris and Hubbard (1989) study the effects of credit rationing on
the adjustment of output in the United States before 1909; and Schwert (1988)
examines the relationship between stock market volatility and real economic
events.

These studies represent useful contributions in the fields of both

economic history and macroeconomics.

THE NEED FOR A NEW INDEX

A potential weakness of much of the research described above, however,
is the quality of the output data employed for the pre-1919 period.

In 1919

the Federal Reserve Board (FRB) began to produce a comprehensive index of
industrial production.

For the period before 1919 there are many monthly

indexes of industrial activity, but all of them have significant problems.
For example, one of the most widely used monthly series is Babson's Industrial
Production Index.1

This index, however, is based on very few physical

production series before 1905.

Instead, for some decades as much as 55

percent of the index is based on the behavior of the gross value of imports

and exports.

As a result, this series is as close to being an index of trade

as it is to being an index of industrial production.

An additional problem

with the Babson index is that, like almost all early series, it is available
only in a seasonally-adjusted form.

This is a drawback because the seasonal

movements in economic activity are themselves interesting and because early
methods of seasonal adjustment involved complicated procedures that typically
did more than simply remove seasonal fluctuations from the data.
A second measure of monthly economic activity that has been widely used
in recent literature is Macaulay's (1938) series on pig iron production.2

The

major drawback of this series is that it is based on only one commodity,
whereas in most settings a more broadly based index would be desirable.
A third measure of monthly production that has been employed recently is
the Index of Production and Trade constructed by Warren Persons (1931).3
index is based very heavily on bank clearings.

This

For studies of the

relationship between money and income this is a particularly serious drawback,
since bank clearings and money are correlated by construction.

In addition,

the coverage of this series changes significantly over the 1877-1918 period.*
This change in coverage may mean that apparent changes in behavior are due to
changes in the data rather than to genuine economic forces.

II.
A.

DERIVATION OF THE NEW INDEX

Procedures
Given the limitations of the existing prewar monthly indexes of

industrial production, we believe there is an urgent need for a new index.

that end, this paper presents a new monthly index of industrial production for
the period 1884-1940.

Our index is derived from reliable and consistent data

on the monthly physical output of thirteen manufacturing and mineral products.
These individual physical output series are combined using value-added weights
to form an aggregate index.

In the derivation of this index no regression

procedures are used and very few adjustments are made to the data.
index is presented only in its seasonally-unadjusted form.

The new

We carry our index

through 1940, despite the existence of the FRB index of industrial production
beginning in 1919, so that we can compare our index with the more
comprehensive FRB series and so that there exists a consistent index that
spans both the prewar and interwar eras.
Many of the specifics of the derivation of the new index, as well as the
actual data, are presented in the Appendix.

Nevertheless, it is useful to

discuss the general procedures briefly.
Data.

We use several criteria for deciding which component series to

collect and to include in our index.

The most important criterion is that the

series be a genuine physical output series or an immediate proxy.

specifically exclude all nominal value series and all indirect proxies for
business activity.
production.

We do, however, often use a close proxy for actual

For example, we often use data on physical shipments rather than

physical production because actual production data are rarely available for
the prewar era.

Similarly, we sometimes use a physical input series to proxy

for the output of a manufactured good.

For example, we use the head of cattle

received by stock yards in Chicago to proxy for the output of dressed beef and
the amount of silk imported as a proxy for the production of silk cloth.
While series on shipments or inputs are obviously imperfect proxies for
genuine production, they should be much more closely related to actual
production than the indirect proxies, such as bank clearings and postage

stamps issued, that have been used by previous authors.

Indeed, one sign that

the series we use are acceptable measures of production is that series like
them are used even today in the derivation of the Federal Reserve Board index
of industrial production.
A second criterion is that we only collect series for which reasonably
reliable and consistent monthly data exist back to 1884.

In particular, all

of the series that we use are based on contemporaneous monthly surveys of
major producers for the entire sample period.

Most of the series that we use

were collected by trade organizations or specialized commercial news
organizations.

For example, the data on pig iron output were collected by

Iron Age, the major trade publication of the iron and steel industry.
Similarly, the data on sugar meltings were collected by Willett and Gray, a
firm that published The Weekly Statistical Sugar Trade Journal.

We restrict

ourselves to long, consistent time series because we specifically want to
avoid splicing together possibly inconsistent series.
Following these criteria, we have collected monthly data on the output
of 13 industrial products for the period 1884-1940.5 The exact series used
and the sources of the data are described in the Appendix.

The series we have

collected cover a wide range of goods. Among the most thoroughly represented
industries are manufactured foods, textile products, iron and steel products,
and petroleum and coal products.

At the same time, however, there are some

industries, such as forest products and stone, clay, and glass products, for
which we have no series.
Adjustment for Production Days.

While we feel it is useful to leave the

individual series seasonally-unadjusted, it is nevertheless desirable to make
the obvious correction for variations in the number of production days per

month.6

To do this, we divide each of the monthly production series by the

number of calendar days in the month less the number of Sundays. While there
was no doubt some variation across industries in the length of the workweek,
especially in the 1930s, our procedure should eliminate most of the
fluctuations in production due solely to fluctuations in production days.
Aggregation.

Aggregating the thirteen individual series into an index

of industrial production is accomplished by converting each series to an
index, and then weighting the individual indexes by value-added weights.

This

is analogous to what is done in the derivation of the modern Federal Reserve
Board index of industrial production.

The original data on value added are

from the Census of Manufactures or the Census of Mining.

Me use the version

of these data given in Fabricant (1940) and Historical Statistics (1975).

choose 1909 as our base year because it is the Census year nearest the middle
of our sample period and because it represents a point of mid-expansion in the
cycle.
While the Appendix discusses specific issues related to the assignment
of weights, the basic strategy is the following.
use the obvious analog in the Census.

For each of our series we

For example, for our series on flour

shipments, we use the value added in flour milling from the Census.
Similarly, for our series on coffee imports, we use the value added in coffee
roasting and spices.

This procedure effectively weights each of our series by

the value added in approximately the two- or three-digit SIC industry of which
it is a component.
B.

Benefits and Cautions
While no new index of industrial production can overcome the fundamental

lack of data for the prewar era, we believe that our new index represents a

substantial improvement over existing indexes. Most importantly, it only
includes data on the output (or occasionally the inputs) of manufacturing
firms and mines; it is not contaminated by other measures of economic activity
such as bank clearings, prices, or foreign trade.

As a result, the index can

be used to test relationships that have occasionally been assumed in the
derivation of other indexes. At the same time, the new index includes a
fairly wide range of industrial commodities.

Thus, it should yield more

information about the behavior of the industrial sector of the United States
than more limited series, such as the widely-used pig iron series.
Second, the new index is based only on output series that are reasonably
consistent over time.

We do not include series that change significantly in

coverage or definition over our sample period.

This procedure ensures that

the index is comparable over the entire sample period.

Thus, it should yield

much more meaningful comparisons of industrial behavior in the late 19th and
early 20th centuries than indexes that are not based on consistent data.
Finally, the new index is much less "adjusted" than other existing
indexes.

Most obviously, we do not seasonally adjust the index.

generally, the new index makes almost no adjustments to the base data.

We do

not use series with gaps that must be filled in by interpolation and do not
impose assumptions about the smoothness of month-to-month variations in
production.

This lack of adjustment means that our new index should be

reasonably uncontaminated by our own beliefs about the likely behavior of
industrial production in the pre-World War II period.
While the new index has many desirable features, it is crucial to point
out that it also has definite limitations.

First, because the index is based

only on series that exist over long sample periods, it will tend to miss new

commodities that are introduced or new data series that become available only
later in the sample period.

This feature of our base data means that the

index may understate the amount of growth that occurred over the entire sample
period because new commodities tend to grow quickly.

It may also provide less

information about the precise nature of overall economic activity later in the
sample period than broader interwar series such as the FRB index.
Another problem is that the focus on consistent, long time series yields
an index that is partially based on close proxies for actual production and
that is biased toward very primary commodities.

The use of input series or

shipments data to proxy for actual output could make the behavior of our index
differ systematically from the true behavior of aggregate production if
inventory fluctuations or production lags are significant.

The bias toward

primary commodities such as pig iron and coal could tend to make our prewar
index more cyclically sensitive than the actual underlying economy because
primary commodities are typically more volatile that highly processed goods.7

III.

COMPARISONS WITH OTHER INDEXES

Having derived a new index of industrial production, it is useful to
compare it with existing indexes. Figure 1 shows the logarithm of our basic
(unadjusted) index for the entire 1884-1940 sample period along with the
Babson and Persons indexes, which are only available in an adjusted form.
Figure 2 shows the new index along with Macaulay's pig iron series and the FRB
index of industrial production, both of which are available without seasonal
adjustment.8

The figures suggest three key facts about how our index compares

with other measures.

First, the new index is considerably more volatile than

the Babson, Persons, and FRB indexes, but less volatile than the pig iron

series.

Second, the new index appears to have seasonal movements that are

roughly as important as those of the pig iron series, but noticeably less
important than those of the interwar FRB series. Third, the new index appears
to display cyclical turning points that are grossly similar to but subtly
different from other series.
A.

Volatility
Table 1 compares the volatility of different indexes by presenting the

standard deviation of the growth rate of each index.

In the case of the

Babson and Persons indexes, the series are available only on a seasonallyadjusted basis, so we examine the volatility of the growth rate of the
published seasonally-adjusted indexes.

For the remaining measures, we examine

the volatility of both the unadjusted indexes and the residuals from a
regression of the change in the logarithm of the index on seasonal dummy
variables.

The regressions are calculated separately for each sample period.

Table 1 shows that our monthly index is more volatile in every sample
period than all of the alternative measures of monthly production, with the
exception of the pig iron series.

This difference in volatility, however, is

almost purely a within-year phenomenon.

Table 1 also shows the standard

deviations of the annual data on our index and the alternative indexes.9
Using annual data, our index is typically only slightly more volatile than the
other indexes, and in some cases it is actually less volatile than the
alternative measures.
The greater month-to-month variation in the new index does not appear to
be due to the fact that our index is seasonally unadjusted:

the same results

hold for the residuals of the regression against seasonal dummy variables.
The most likely explanation for the difference is that the seasonal-adjustment

procedures used by Babson and Persons may smooth away more of the short-run
variation in the data than does a simple regression against monthly dummy
variables.

This is a common feature of older seasonal-adjustment procedures.

While the method of seasonal adjustment is almost surely very important,
some of the difference in volatility between our index and alternative indexes
is no doubt also due to differences in the commodities represented by our
index.

The fact that we exclude series such as bank clearings or exports may

affect the volatility of our index if those series have particular volatility
characteristics.

Furthermore, in comparison to the FRB index for the interwar

period our index is simply based on a smaller number of commodities.10

there is variation in each individual component that is not perfectly
correlated across series, an index based on a greater number of individual
series will display less volatility, holding the volatility of individual
series constant.

This fact may explain why the FRB index for 1919-1940 is

less volatile than our new index even though both series are unadjusted.11

may also suggest that our index provides a less good indication of aggregate
volatility for the interwar era than the FRB index.
The finding that our index shows more month-to-month variation than
other indexes may alter economists' view of the pre-World War II economy.

particular, the fact that at least 13 industrial commodities appear to have
fluctuated quite significantly over very short time periods may indicate that
the prewar economy was buffeted by frequent shocks.

That these shocks

resulted mainly in intra-year fluctuations in real production may suggest that
the economy had a rapid, though not instantaneous, self-righting mechanism.
While the new index clearly has different volatility characteristics
from most of the existing measures of industrial production, it is useful to

note that nearly all of the indexes show a similar change in volatility
between the prewar and interwar eras.

Table 1 shows that every monthly index

except the Persons Index of Production and Trade is more volatile after 1914
than before.12

This finding mainly reflects the fact that the Great

Depression is in the post-1914 sample period.

However, even during the

relatively tranquil 1922-1928 period the new index, like most others, is
slightly more volatile than during the pre-1914 period.13

This may suggest

that the various structural changes that occurred around World War I, such as
the rise of the Federal Reserve System, may have affected the volatility of
the U.S. economy.
B.

Seasonality
As mentioned above, purely seasonal movements appear to explain very

little of the total month-to-month variation in our new index.

This can be

seen from the finding that the R2 of the regression of the growth rate of the
new index on seasonal dummy variables is .17 in the pre-1914 period and .04 in
the post-1914 period.

This is not to say, however, that seasonal movements in

the new index are unimportant.

Rather, the seasonal pattern in both periods

is large in magnitude, with an amplitude of 10 percent in the pre-1914 period
and 6 percent in the post-1914 period.

It is also statistically significant

at at least the 10 percent level in both periods.
A comparison of the seasonal behavior of the new index with the two
other available unadjusted series indicates that the new index has seasonal
movements that are similar in importance to those of the pig iron series, but
distinctly less important than those in the interwar FRB index.14

The similar

importance of seasonal movements in the new index and the pig iron series
could be due in part to the fact that pig iron is weighted heavily in the new

index.

However, this cannot be the entire explanation because the pattern of

seasonal movements is quite different in the two series.

In particular, the

new index does not show the surge in production early in the year that is
characteristic of the pig iron series, though it does show the surge in the
fall that is also present in the pig iron series.
Examination of the seasonal behavior of the 13 individual series that
make up the new index indicates that some of the apparent unimportance of
seasonal fluctuations in the new aggregate index stems from the cancelling out
of individual seasonal patterns.

For some of the individual series seasonal

fluctuations are quite important, accounting for as much as 30-40 percent of
the total variation in growth rates.

However, the seasonal patterns differ

quite substantially across the series.

For example cattle receipts and flour

shipments have a large seasonal peak in the fall, wool receipts have a peak in
the summer, and sugar meltings are very low in the fall but rise dramatically
in the winter months.
The fact that the Federal Reserve Board index of industrial production
shows a stronger aggregate effect of seasonal fluctuations in the interwar era
may reflect the different composition of the FRB index.

In particular, our

index is more heavily weighted toward manufactured foods, which tend to have
distinct seasonal cycles that vary greatly from product to product.

Thus, our

index may have more cancelling out of seasonal patterns than the FRB index,
which includes many more heavy industrial goods.

One factor that does not

appear to account for the greater importance of seasonal movements in the FRB
index is the fact that the FRB index is only available after 1919.

Between

the prewar and interwar eras there is some change in the seasonality of the
new index, but that change goes in the direction of making the seasonal less

important over time.
To the extent that the relative unimportance of seasonal fluctuations
shown by the new index is a genuine phenomenon, this finding suggests that in
the period before 1940 a very small fraction of aggregate short-run
fluctuations were attributable to predictable seasonal shocks and a very large
fraction were due to unpredictable shocks. The most obvious place to look for
such unpredictable shocks would probably be random demand shocks from both
foreign and domestic sources.

The importance of unpredictable shocks in the

prewar era presents an interesting contrast to the postwar era, where as much
as 80 percent of the total variation in the monthly growth rate of industrial
production is due to predictable seasonal factors.15
C.

Turning Points
The graphs in Figures 1 and 2 indicate that the turning points of our

new index are grossly similar to those of the other indexes of production.

particular, our index, like all of the others, shows significant recessions in
1893-94, 1907-08, 1920-21, 1930-33 and 1937-38.
At the same time however, there are some important differences between
our index and various alternatives.16

These differences are especially

noticeable around the dates of major financial panics.

For example, following

the banking panic of May 1884, our index shows a much more immediate fall in
production than does the pig iron series that has been used in other analyses.
This is also the case around the panic of October 1907. The new index, like
the Babson and Persons indexes, turns down perceptibly before the panic,
whereas the frequently used pig iron series does not turn down until November
1907.

These differences in the timing of real output movements around

financial panics could have large effects in studies that seek to identify the

direction of causation between panics and recessions.
A more general way to analyze the turning points of our index is to
compare them to the NBER reference dates for business cycle turning points.
With respect to the most severe recessions (1893-94, 1907-08, 1920-21, 193033, 1937-38), our index shows peaks and troughs that are quite close to those
of the traditional NBER dating.

In the case of the less virulent downturns,

however, our index frequently has peaks and troughs that differ from the NBER
dates by several months.

For example, the NBER dates a peak in 1899:3 whereas

our index continues rising with only slight interruption until 1899:11. These
differences may be due to the fact that the NBER uses both real output and
other indicators, such as interest rates and prices, to date cycles.
Nevertheless, the finding that our new index has turning points that are
sometimes quite different from NBER reference dates may alter how economists
should date fluctuations in real output in the prewar era.

Thus, as with the

other characteristics of economic fluctuations examined in this paper, the
derivation of a new index of industrial production may lead to new views about
the pre-World War II macroeconomy.

APPENDIX

This appendix describes the sources, availability, and necessary
adjustments for the thirteen individual production series that are used in the
derivation of the Miron-Romer index. It also describes the procedures used to
combine the individual series into an aggregate index of industrial
production.

Component Series

As described in the text, the individual series that we include in the
new index come from a variety of primary sources. Many of the series that we
use, however, have previously been collected by researchers of the National
Bureau of Economic Research. These series are available on a tape entitled
"National Bureau of Economic Research: Macroeconomic Time Series for the
United States, United Kingdom, Germany, and France" that is stored at the
Inter-University Consortium for Political and Social Research (ICPSR 7644).
In cases where the series we use is available on the NBER tape, we use
the information in the following way. We first compare the data on the tape
to the original source documents. Whenever there is a discrepancy between the
tape and the original source, we update the series to be consistent with the
primary source. We do this because there appear to be numerous coding errors
in the tape. The only time when we do not follow this rule is when there is
an obvious typographical error in the original source.
After checking and updating the data on
further adjustments that are necessary to the
no adjustments to make. In other cases there
reported or the in the coverage of the series

the tape, we then make any
series. In some cases there are
may be a change in the units
that must be dealt with.

In what follows we describe the coverage and nature of each series and
the appropriateness of using a given series as a measure of production. We
then discuss the primary sources of the series that we use and all of our
transformations of the data on the NBER tape into the series that are
ultimately used to form the new aggregate index. The name listed for each
series is the mnemonic under which the series is stored in a RATS data file
that is available on diskette from the authors.

A. Pig Iron Capacity
PICAPM
Gross Tons (2240 Pounds) Per Day, Monthly Average
Available 1884:6 - 1940:12

Basic Facts about the Series

This series shows the daily operating capacity of the average number of
pig iron furnaces in blast in a given month. Since blast furnaces are not
turned on and off frequently and are generally run at full capacity, this
series should be closely correlated with the actual production of pig iron.
Indeed, after 1902 when production data become available the correlation
between production and capacity is nearly perfect. Furthermore, since pig iron
is the primary input to the production of steel, this series should also
provide a good proxy for basic steel production, though there may be a lag
between the production of the two goods.
2.

Original Source

The base data are from Iron Age, the major trade publication of the iron
and steel industry, and are based on a comprehensive survey of iron and steel
producers. The data available from Iron Age show the daily operating capacity
of furnaces in blast on the first of the month. These data are usually broken
down by the fuel used in the furnace: charcoal, anthracite, and coke. Since
for some purposes the data on the capacity of charcoal furnaces are not
reported, we restrict our data collection to the capacity of anthracite and
coke furnaces. After 1930 the only data available are for the capacity of
coke furnaces. Since this appears to be due to the fact that anthracite went
out of use, we do not view this as a change in the coverage of the series and
do not make any adjustment for it.
3.

Transformations

This series is not available from the NBER tape and so was entered by
hand. Whenever a capacity number for a given month was revised from one issue
of Iron Age to another, we use the latest version as the true number.
To use the series we have as a proxy for monthly production, it is
necessary to average the first day of the month observations to yield average
daily capacity for the month. The formula that we use to do this is:
Monthly average -

[l/(2*Dayst) ] [(Dayst + l)Capt

(Dayst - l)Capt+1]

where Days is the number of days in the month and Cap is the daily capacity on
the first of the month.
There are missing observations for 1918:2 and 1921:2 that cannot be
eliminated because the data just are not available. To deal with this, we use
the following procedure. We take the first of the month capacity observations
for January and March and distribute the average of these two numbers over two
months, using a formula analogous to that above. The actual formulas are:
January:

44/59*Capacity(Jan 1) + 15/59*Capacity(March 1)

February:

29/118*Capacity(Jan 1) + 89/118*Capacity(March 1).

Anthracite Coal Shipments

ACSHIP
1000s of Long Tons (2240 Pounds) Shipped Over the Month
Available 1884:1 - 1940:12, with 1924:1-1926:12 missing

Basic Facts about the Series

This series shows the shipments of anthracite coal over a given month.
This series should be highly correlated with actual production because
inventory holdings at the mines tended to be fairly small in the period
covered by our index.
2.

Original Sources

The base data are from The Coal Trade (later called Sawards Annual), a
major industry publication. The data, at least after 1905, appear to have
been originally collected by the Anthracite Bureau of Information and are
based on the reports of carrier companies. For some unknown reason no data
were collected for the period 1924:1-1926:12. Beginning in 1933, the data are
reported in net, rather than long tons.
3.

Transformations

This series is available for 1884:1-1922:12 from the NBER tape (variable
212 of dataset 01A). We have checked the data on the tape against the
original source. For the period 1884-1922 there are few discrepancies between
the tape and the original source. In some years the original data change from
one issue of The Coal Trade to another, presumably due to data revisions. As
is appropriate, the NBER uses the later numbers. In 1903:12, 1913:11,
1915:4,8 and 1919:9 the numbers from The Coal Trade and the tape differ.
However, it appears that there may be typos in the original source. We deduce
this from the fact that the monthly numbers given in The Coal Trade do not add
up to the annual figure while the NBER numbers do. This suggests that the
NBER had additional information and that their numbers should be used.
For 1918-1922 we have data from both The Coal Trade and Saward's Annual.
In 1918:1, 1920:3, 1922:9-10, and 1922:12 the numbers from the two sources
differ slightly and the tape accords with The Coal Trade. We update the data
on the tape to be consistent with Saward's Annual because after 1922 this is
the only original source available. We also convert the data after 1933 from
net tons to gross tons so that it is in the same units as the earlier data.
While the NBER codes the data for 1922:4-8 as missing because of the
strike, we record coal shipments as zero in these months. A footnote in
Saward's Annual states: "No shipments in April, May, June, July, and August,
1922, on account of strike." Furthermore, for this period we have actual
production data and it is essentially equal to zero.

Crude Petroleum Production. Appalachian Region

APPAPETR
1000s of Barrels Per Day
Available 1884:1 - 1940:12

Basic Facts about the Series

This series shows the average daily production of crude petroleum in the
Appalachian region in a given month. This region is composed of New York,
Pennsylvania, Kentucky, West Virginia, and parts of Ohio. For the period
1884-1890, data are only available for New York and Pennsylvania. This
represents only a slight change in coverage because New York and Pennsylvania
account for over 90% of Appalachian production in this period. Both petroleum
series are on a daily average basis, where daily average has been calculated
as monthly production divided by the number of days in the month.
For the late 1800s Appalachian production represents nearly 100% of
total U.S. petroleum production (based on a comparison of available annual
numbers). However, by World War I, Appalachian production accounts for only
about 10% of total production. This suggests that the Appalachian series
becomes a decidedly less good proxy for total production over time.
2.

Original Sources

The base data are from Mineral Resources and Minerals Yearbook. These
figures were compiled by the U.S. Geological Survey from monthly reports of
pipe-line and other companies. They show the quantity of petroleum
transported from producing properties. Beginning in 1919 there appears to
have been some adjustment for stocks.
3.

Transformations

This series is available from the NBER tape. It is presented as two
series, variable 243 and variable 241 of dataset 01A. Variable 243 begins in
1894:1 and covers production in the entire Appalachian region and variable 241
extends back to 1884:1 and covers production only in New York and
Pennsylvania.
Mineral Resources provides data on Appalachian production for 1890:1 1893:12 as well as the later data that appear on the tape. There is no
evidence of any change in coverage or alteration of procedures. Therefore, we
simply continue the tape series for Appalachian production with this series.
We have checked the data against the original source.
Minor
discrepancies in the New York and Pennsylvania series exist in 1884:2 and
1884:7. These discrepancies appear to be due to rounding errors. The data on
the tape are updated to be consistent with the original source. Discrepancies
in the Appalachian series exist in 1921:2 and 1924:1 and these are corrected
to be consistent with the original source. In addition, there are many
discrepancies between the tape and the original sources in 1935-36 and 1937

and 1939. The discrepancies are always very small. The likely source of the
discrepancies for 1935-36 is that Minerals Yearbook only gives data by state
and hence it is impossible to aggregate exactly in the way that is done
earlier for the Appalachian Region. The discrepancies for 1937 and 1939 are
most likely due to data revisions that we cannot document. Evidence in favor
of this is that the numbers from Minerals Yearbook say subject to revision.
Furthermore, one can see that there are substantial revisions for 1937 and
1939 because the annual numbers given for those years in 1938 and 1940 are
quite different from the preliminary numbers. This is not true of the numbers
for 1938 and 1940 for which our numbers are identical to the NBER tape.
Because it seems likely that the tape has revised data, we accept the tape's
numbers for 1935-1940.
While NY and PA production account for almost all of Appalachian
production prior to 1890, it is sensible to connect the two series with a
simple ratio splice to ensure that there is no jump in the combined series.
The year chosen for the splice is 1890:1 and the splice was done in levels.
The procedure resulted in multiplying the NY and PA series for 1884:1 1889:12 by (70.0/68.0).

Sugar Meltings at Four Ports

SUGM4
1000s of Long Tons (2240 Pounds) Melted over the Month
Available 1890:1 - 1937:12

Basic Facts about the Series

This series shows the amount of raw sugar being made into refined sugar
at the four main ports of sugar import. As such, it should provide an
excellent proxy for the actual production of refined cane sugar.
2.

Original Source

The base data are from Willett and Gray's Weekly Statistical Sugar Trade
Journal, the major trade publication of the sugar industry. For most of the
late 19th and early 20th centuries the Weekly Statistical Sugar Trade Journal
tracks only the meltings in four Atlantic ports: New York, Boston,
Philadelphia, and Baltimore. Following World War I, the Journal also tracks
meltings in eight or more ports. Because the four-port series continues
through 1937, we opt to use this consistent series for the entire sample
period. However, while for the early period it is clear that 4 ports covered
most of the meltings, by 1940, they appear to account for only about 50
percent of all meltings. This means that our procedure may cause us to miss
some of the expansion in the sugar industry in the interwar period.
3.

Transformations

This series is available from the NBER tape (variable 392 of dataset
01A) for the period 1890:1-1921:12.

We expand the NBER series using monthly data from the original source to
cover the period 1922:1-1937:12. The monthly data stop in 1937:12. Though
there are weekly data on meltings at the Atlantic ports for 1938-1940, it is
difficult to use these because no observations exist for the last week of each
year. Neither carrying forward the previous week's growth rates or adopting
the first week of January's growth rate seem to be appropriate. In
particular, using either of these procedures for the eight-port series (for
which both monthly and weekly data exist) yields a reading for December that
is higher than the actual monthly observation.
We have checked the data on the NBER tape against the original sources.
Minor discrepancies exist for 1897:3,4, 1898:10,11 and 1919:12. The data on
the tape are corrected to be consistent with the primary source.

Cattle Receipts at Chicago

CATREC
1000s of Head Received During the Month
Available 1884:1 - 1940:12
1.

Basic Facts about the Series

This series shows the head of cattle received in Chicago in a given
month. Beginning in 1907 there exist actual data on cattle slaughtered. The
receipt series appears to be highly correlated with slaughter, suggesting that
cattle receipts are a good proxy for the production of dressed beef. The
receipt series probably becomes a less good proxy for total slaughter over
time because Chicago declines in importance.
2.

Original Source

The base data are from the Chicago Board of Trade Annual Report.
According to the source, the data show receipts at stock yards in Chicago and
are derived from reports of the U.S. Department of Agriculture, terminal
elevators, and transportation agencies. We obtained Xeroxes of the Annual
Reports that were missing from Baker Library directly from the Chicago Board
of Trade Records Center.
3.

Transformations

This series is available from the NBER tape (variable 312 of dataset
01A). We have checked the data on the tape against the original source.
Minor discrepancies were found in 1930:1, 1935:9, and 1940:9 and these were
resolved in favor of the original source.

Hog Receipts at Chicago

HOGREC
1000s of Head Received During the Month
Available 1884:1 - 1940:12

Basic Facts about the Series

This series shows the number of hogs received in Chicago in a given
month. Beginning in 1907 there exist actual data on hogs slaughtered. The
receipt series appears to be highly correlated with slaughter, suggesting that
receipts are a good proxy for slaughter. The receipt series probably becomes
a less good proxy for total slaughter over time because Chicago declines in
importance.
2.

Original Source

Transformations

This series is available from the NBER tape (variable 306 of dataset
01A). We have checked the data on the tape against the original source.
Discrepancies were found in many years: 1885:7, 1889:2,6, 1893:2, 1894:1,2,7,
1895:3,11, 1898:1, 1899:1, 1900:1,5, 1901:4, 1902:10 1903:5, 1906:9, 1908:6,
1909:3, 1912:1, 1913:12, 1915:5,12, 1918:6, 1921:3, 1922:10, 1923:9, 1926:4,
1929:5, 1932:9, 1933:6, 1934:6. These appear to be due to rounding errors and
some very large typographical errors in the NBER series. We resolve all
discrepancies in favor of the original source.

Connellsville Coke Shipments

COKESHIP
Net Tons (2000 Pounds) Shipped during the Month
Available 1894:1 - 1935:12

Basic Facts about the Series

This series shows the tons of coke shipped from the Connellsville region
of Pennsylvania. This series should provide a good indicator of total coke
shipments in the U.S. because the Connellsville region accounted for at least
50 percent of total production in the pre-1940 period. Furthermore, because
beehive coke was ordinarily not stored at the ovens in any great quantity,
shipments should provide a reasonable proxy for actual production.
2.

Original Sources

The base data are from Mineral Resources and Minerals Yearbook. For the
period before 1928 the data in these sources are taken directly from the
Connellsville Courier: for the period 1929-1935 the data are compiled by the
Bureau of Mines from the weekly reports of the Courier. No data on

Connellsville shipments are available before 1894 or after 1935.
3.

Transformations

This series is not available from the NBER tape and so was entered by
hand directly from the original source. No adjustments needed to be made to
the base data.

Minneapolis Flour Shipments

FLRSHIP
1000S of Barrels Shipped from Minneapolis During the Month
Available 1884:1 - 1940:12

Basic Facts about the Series

This series shows the shipments of flour out of Minneapolis. Though many
of the major mills were based in Minneapolis, it appears that on an annual
basis, shipments out of Minneapolis accounted for only 10-20% of total wheat
flour production. However, the annual total production data on which this
calculation is based are quite imprecise. Furthermore, since flour milling is
to some extent affected by agricultural production, one could reasonably
expect Minneapolis shipments to be similar to total shipments. Indeed, after
World War I monthly data on total production become available and the
correlation between this series and Minneapolis shipments is quite high.
2.

Original Source

The base data are from the Annual Report of the Chamber of Commerce and
Board of Trade of the City of Minneapolis. We obtained Xerox copies of the
Annual Reports from the Minneapolis Chamber of Commerce.
3.

Transformations

This series is available from the NBER tapes (variable 379 of dataset
01A). We have checked the data on the tape against the original data.
Several discrepancies were found. Those for 1885:3, 1886:9-11, 1887:9,
1889:7-12, 1902:5, 1905:12, 1907:9, 1920:9 and 1937:12 were resolved in favor
of the original source. Most of these errors appear to be due to the fact
that the NBER took the data from a summary volume in 1899 that had many
errors. We have used the yearly reports instead. For 1920:6 we use the NBER
number because it is in accord with the weekly and annual data in the Annual
Report, whereas the original monthly number is not. Parts of 1886 and 1905
could not be checked because the original sources were missing or failed to
report the necessary numbers.

Wool Receipts at Boston

WOOLR
Millions of Pounds Received in Boston (Domestic and Foreign) per Month
Available 1888:1 - 1940:12

Basic Facts about the Series

This series shows the amount of raw wool coming into Boston from both
domestic and foreign producers. As late as the 1930s receipts at Boston
accounted for around one half of the total domestic wool clip and between 20
and 40 percent of foreign imports. Provided that inventories of raw wool are
not large and do not have strong cyclical properties, this series should
provide a reasonably good proxy for the output of woolen cloth.
2.

Original Sources

The base data for 1888-1918 are from the Boston Chamber of Commerce
Annual Report. For the period after 1918 the data are from the Survey of
Current Business. The data in the Survey are described as being from the U.S.
Department of Agriculture, but the U.S.D.A. compiled its data from the Boston
Commercial Bulletin and the records of the Boston Grain and Flour Exchange.
Thus, the series from the two sources appear to be based on similar underlying
reports.
3.

Transformations

Data on wool receipts are available from the NBER tape. Variables 163
and 165 of dataset 01A show the receipts of domestic wool in Boston per month.
The two series differ in that the earlier series is measured in bales and the
later series is measured in pounds. Variables 167 and 169 of dataset 01A show
the receipts of foreign wool in Boston per month. Again the two series differ
in the units in which wool is measured.
We have checked the data on the tape against the original sources.
There were no major discrepancies, but there were many apparent rounding
errors. We have corrected all of these discrepancies so that the various
series are consistent with the original sources.
The fact that the units of measurement for both the domestic and foreign
receipts series changed from bales to pounds in 1900 should not present a
problem because bales were of a nearly standard weight and because there is
some overlap in the series. Thus, we combine the two series for each type of
receipts by ratio splicing the series in bales to the series in pounds in
1900:1. This amounts to multiplying the early domestic series (variable 163)
for 1888:1-1899:12 by 7.26/29.03 and the early foreign series (variable 167)
for 1888:1-1899:12 by 5.23/10.47.
We aggregate the foreign and domestic series into a series on total wool
receipts. Since the domestic and foreign series are in the same units, a
simple sum is appropriate.

Coffee Imports

COFFEE
Millions of Pounds Per Month
Available 1884:1 - 1940:12

Basic Facts about the Series

This series shows the amount of raw coffee imported into the U.S.
Because the U.S. does not grow coffee domestically, coffee imports should
provide a comprehensive measure of the inputs to the coffee roasting and
grinding industry.
2.

Original Sources

The data on imports are from the Monthly Summary of the Foreign Commerce
of the U.S. that is published by the Bureau of the Census. This source had
different titles and different publishing agencies in the period before 1914.
Among the alternative titles are Monthly Reports on the Commerce and
Navigation of the U.S.. Monthly Summary of Commerce and Finance of the U.S..
and Monthly Summary of the Imports and Exports of the U.S. Libraries
typically bind these various publications into one volume for the entire
period that is labeled the Monthly Summary of the Foreign Commerce of the U.S.
and there is no apparent change in coverage or the procedures used over time.
For simplicity we refer to the original source in the following discussion as
simply the Monthly Summary of the Foreign Commerce.
The only problem we have noted with the base data is that because of
tariff changes, the observations for 1913:9 and 10 and 1922:9 and 10 may be
erroneous. Specifically, imports for the first three days of 1913:10 are
included in the September 1913 figure and imports for the last 8 days of
1922:9 are included in the October 1922 figure.
We do not to try to correct these observations because simple prorating
of the data seems likely to yield more erroneous observations. The Underwood
Tariff of 1913 lowered average rates on October 4th. Hence, one would not
expect much importation in the first 3 days of October when tariffs were still
high. Thus, it is of no consequence that these observations are included in
the September figure. Similarly, in 1919 the Fordney-McCumber Tariff raised
average rates on September 22nd. In this case, one would not expect much
importation in the last days of September. Therefore, it does not matter that
those days are included in October.
3.

Transformations

This series is available from the NBER tape (variable 93 of dataset
07A). We have checked the NBER data against the original source. (Note: We
have not checked the data for 1885:7-1886:6, 1887:7-1888:6, and 1939:7 because
we were unable to obtain the source documents.) There were minor
discrepancies between the NBER series and the Monthly Summary in 1903:2,
1906:9, 1909:4, 1915:8, 1932:2, and 1936:3. These observations were changed

to be consistent with the original source.
for the series.
K.

No further corrections are needed

Tin Imports

TIN
Long Tons (2240 Pounds) Per Month
Available 1884:1 - 1940:12

Basic Facts about the Series

This series shows the amount of bars, blocks, pigs, grain, and
granulated tin imported into the U.S. as well as the tin content of imported
ore. It is used to proxy for the output of refined tin and tin products
produced in the U.S.
2.

Original Sources

The data on imports are from the Monthly Summary of the Foreign Commerce
of the U.S. See the original sources section for the coffee import series for
a more thorough description of this source.
One problem noted by the NBER was that the first 3 days of October 1913
were included with the September number. We do not do anything about this
because average tariffs were dropped on October 4, 1913. Hence, one would not
expect many imports during the first 3 days of October.
3.

Transformations

This series is on the NBER tape (variable 107 of dataset 07A). We have
checked the data on the tape against the original source. (Note: We have not
checked the data for 1885:7-1886:6, 1887:7-1888:6, and 1939:7 because we were
unable to obtain the source documents.) To check the data on the tape, the
data from the Monthly Summary of the Foreign Commerce had to be treated in the
following way. The data in pounds was first rounded to the nearest 1000 and
then divided by 2.24 to yield long tons. This number was then rounded to the
nearest integer. This way of dealing with the data yielded a series that
matched the NBER in most years. There were minor discrepancies in 1901:6,
1901:11, 1912:4, 1921:11, 1923:6, 1926:4, 1928:5, 1929:10, 1930:2, 1931:1,4,8,
1933:1,8, 1934:8,11, 1938:6, 1939:4,8,and 9. These were corrected to be
consistent with the original source. The NBER codes the observation for
1893:7 as missing. However, the data from the Monthly Summary indicate that
imports simply rounded to zero in this month. Consequently, we have replaced
the missing value code with a zero for this observation.

Crude Rubber Imports

RUBBER
Millions of Pounds
Available 1884:1 - 1940:12

Basic Facts about the Series

This series shows the amount of crude rubber imported. It is used to
proxy for the output of rubber goods such as tires and rubber shoes produced
in the U.S.
2.

Original Sources

The data on imports are from the Monthly Summary of the Foreign Commerce
of the U.S. See the original sources section for the coffee import series for
a more thorough description of this source.
The only problem with this series is that the Monthly Summary tracks
somewhat different products in different periods. After 1910:7 the Monthly
Summary numbers cover the imports of India rubber. For the period 1890:101912:12, the Monthly Summary data include both India rubber and guayule gum.
For the period 1884:1-1892:12, the Monthly Summary data include India rubber,
guayule gum, and gutta percha. A comparison of the various series in the
short periods of overlap suggests that the behavior of the different series is
very similar. Thus, it seems reasonable to join the series together to form a
single rubber imports series. However, some correction for changes in levels
is necessary because imports of guayule gum are nontrivial.
3.

Transformations

The rubber imports data are available from the NBER tape. The tape
includes three series: variable 101 of dataset 07A covers the period through
1892:12 and includes data on India rubber, gutta percha, and guayule gum;
variable 103 of dataset 07A covers the period 1890:10-1912:12 and includes
data on India rubber and guayule gum; and variable 105 of dataset 07A covers
the period 1911:1-1940:12 and includes data only on India rubber.
The Monthly Summary of the Foreign Commerce contains the data necessary
to carry the series covering only India rubber (variable 105) back to 1910:7.
We update this series with these additional observations.
We have checked the data against the original source. (Note: We have
not checked the data for 1885:7-1886:6, 1887:7-1888:6, and 1939:7 because we
were unable to obtain the source documents.) Minor discrepancies were found
in 1896:4, 1900:7, 1926:5, 1932:8,9,11, 1935:3,4,8,11, 1936:8, 1937:8, and
1940,8,10. These observations were updated to be consistent with the original
source.
The three different variants of the rubber import series are spliced
together so that there are no jumps in the series. We choose to leave the
longest series (variable 105) unspliced and to splice the other series to it.
Specifically, we use the corrected and expanded variable 105 without further
adjustment for 1910:7-1940:12. For 1890:10-1910:6 we splice variable 103 to
variable 105 by multiplying variable 103 by the ratio of variable 105(1910:7)
to variable 103(1910:7). For 1884:1-1890:9 we splice variable 101 to the
already spliced series. That is, we multiply variable 101 by the ratio of the
already spliced series (1890:10) to variable 101(1890:10).

Raw Silk Imports

SILK
1000S of Pounds per Month
Available 1884:1 - 1940:12

Basic Facts about the Series

This series shows the amount of raw silk imported into the U.S. Since
domestic silk production is minimal, this series should provide a
comprehensive measure of the raw materials input to the production of silk
cloth.
2.

Original Sources

The data on imports are from the Monthly Summary of the Foreign Commerce
of the U.S. See the original sources section for the coffee import series for
a more thorough description of this source.
The only issue surrounding this series involves a slight change in
coverage in the early 1920s. For the period up to 1924:12 the data available
on raw silk imports include re-exports. Beginning in 1919:1 data excluding
re-exports are available. This change is fairly minor because re-exports are
very small, and because the two series behave very similarly in the five-year
period when both exist. Nevertheless, it is sensible to ratio splice the two
series together to prevent a slight discontinuity in the final data.
3.

Transformations

The silk imports data are available from the NBER tape. Variable 131 of
dataset 07A covers the period 1884:1-1924:12 and includes re-exports.
Variable 133 of dataset 07A covers the period 1919:1-1940:2 and excludes reexports .
We have used the Monthly Summary to extend variable 133 through 1940:12.
In doing this we have followed the NBER procedures: we first round all the
components to 1000s, then add raw silk imports and waste (the numbers on
cocoons are not included) and then subtract off re-exports.
We have checked the data on the tape against the original source. Many
minor discrepancies were found and the data from the tape were corrected to be
consistent with the original source.
To deal with the minor change in coverage we use a ratio splice in
1924:12. We choose to leave the longer series (variable 131) unspliced and to
splice variable 131 to it. Specifically, variable 131 for 1884:1-1924:12 was
left unadjusted and variable 133 for 1925:1-1940:12 was multiplied by the
ratio of variable 131(1924:12) to variable 133(1924:12).

II.

Procedures Used to Fora the Index of Industrial Production

The aggregation of the thirteen series we have collected into an index
of industrial production involves several additional steps. These steps
include adjustment for production days, conversion of each series to an index,
and combination of the individual indexes using value-added weights.

Adjustment for Production Days

With the exception of the series on petroleum production (APPAPETR) and
pig iron capacity (PICAPM), all of our series show total output over the
month. As a result, the series show predictable upticks in long months and
downticks in short months. To remove this effect it is desirable to generate
a series that is on a per production day basis. This is consistent with what
is done today in the derivation of the Federal Reserve Board's seasonallyunadjusted Index of Industrial Production.
To make this adjustment we do the following. First, no correction is
needed for the pig iron series. This is true because PICAPM shows the daily
capacity of the average number of furnaces in blast during the month. Thus,
it is already on a per day basis. Furthermore, because blast furnaces were
not turned off for weekends, no adjustment is needed for the difference
between calendar days and production days.
Second, the petroleum series in the original source is calculated as the
monthly production of petroleum divided by the number of days in the month.
To put it on a production day basis we first multiply the base series by the
number of calendar days in the month and then divide by the number of
production days.
All other series are put on a production day basis by dividing by the
number of production days. The number of production days is calculated as the
number of calendar days minus the number of Sundays. We do not exclude
Saturdays because it appears that most industries worked six days a week until
at least the mid-1930s.

Forming Individual Indexes

As described below, we use 1909 as the base year for our index. That
is, 1909 is the year that is set equal to 100 for each individual index and
for the aggregate index. We form the individual indexes by dividing the
monthly observations for each series (adjusted for production days) by the
average monthly value of that series in 1909.

Value-Added Weights

To combine the individual indexes into an aggregate index of industrial
production we use value-added weights. Value-added weights are used because
we want the index to represent the output of manufacturing industries. Thus,

it should be irrelevant whether the inputs were expensive or cheap.
The original data on value added are from the Census of Manufactures or
the Census of Minerals. We use the version of these data given in Fabricant
(1940, Appendix C) and Historical Statistics (1975), For most categories
Fabricant is identical to the Census.
1909 is chosen as the base year because it is the Census year nearest
the middle of our sample period and because it is an ordinary year. Unlike
1904 and 1914 which were recession years, 1909 represents a point of midexpansion in the cycle. Using a base year near the center of the sample
period is desirable because it limits the importance of the compositional
changes over time. Using a year of mid-expansion ensures that the
differential effects of depressions will not affect the weights for the entire
sample period.
The allocation of value added to various series is fairly straightforward. For most of our series there is an obvious analog in the Census.
For example, for our series on flour shipments from Minneapolis, we use the
value added in flour milling from the Census. Following this procedure, we
end up weighting our series by the value added in approximately the
corresponding two- or three-digit SIC industry. The following list shows the
category from Fabricant used for each of our series. It also discusses any
special adjustments that needed to be made to the Fabricant data.
1. Pig Iron Capacity. We assign this series the value added in blast furnace
products and steel mill products. We include the value added of steel mills
because pig iron capacity is a good proxy for steel production and because the
value added in pig iron production alone does not adequately reflect the
importance of this series. It is useful to note that the steel mill products
category only includes very crude steel goods; it does not include highly
fabricated goods such as machines.
2. Anthracite Coal Shipments. Historical Statistics gives the value added in
coal mining. We divide this between anthracite and bituminous coal using the
data on the gross value of anthracite and bituminous coal output also given in
Historical Statistics. This procedure indicates that anthracite should be
given 26.9% of the total value added in coal production.
3. Crude Petroleum Production. Appalachian Region. We assign this series the
value added in petroleum refining, lubricants not elsewhere mentioned, and
oils not elsewhere classified.
4. Sugar Meltings in Four Ports. We use the value added in cane sugar
refining and cane sugar, not elsewhere mentioned.
5. Cattle Receipts at Chicago. Fabricant lists the value added in meat
slaughtering. Even the original Census documents do not divide this value
added among the various types of animals. To derive separate weights for
cattle and hogs, we allocate the total value added in meat packing according
to the gross value of the beef and pork (fresh and cured) produced in 1909.
These data are available from the Census of Manufactures for 1914. Based on

the gross value of the output, the total value added in meat packing is
divided by giving 41.1% to cattle and 58.9% to hogs.
6. Hog Receipts at Chicago.

See the description of cattle receipts.

7. Connellsville Coke Shipments.
coke oven products.
8. Minneapolis Flour Shipments.
flour industry.

We assign this series the value added of

We assign this series the value added in the

9. Wool Receipts at Boston. We assign this series the value added in woolen
goods, worsted goods, wool pulling, and wool scouring. We chose these series
because they seemed related to very basic wool cloth, not the more finished
goods like clothing and carpet also listed in Fabricant.
10. Coffee imports. We use the value added in coffee roasting and spices.
This series is listed on p. 637 of Fabricant (1940).
11. Tin Imports. We assign this series the value added of tin and other
foils, tin cans, tinware not elsewhere classified, and tin and terne plate.
12. Crude Rubber Imports. We assign this series the value added of all
rubber products. This seems appropriate because the only subcategories listed
are rubber shoes and tires and tubes. There is no category such as basic
rubber.
13. Raw Silk Imports. Fabricant and the Census only give the value added of
silk and rayon goods combined. To isolate the part appropriate to silk, we
use Fabricant's data on the gross value of silk and rayon products (from
Appendix B). We divide the products according to fabric content; those
products for which fabric content were unclear were simply excluded from the
calculations. This procedure may understate the importance of silk in the
earlier period because a larger than average amount of velvet and upholstery
material may have been made of silk. This procedure suggests that 74.9% of
the value added in silk and rayon is safely attributable to silk.
The value-added figures assigned to each series are used to derive
weights that sum to one. The base data and the weights are given in Table Al.
These weights are then used to combine the individual indexes into an
aggregate index.

Adjustment for Missing Values

While many of our series exist from 1884-1940, there is a certain amount
of attrition at both ends of the index. There is also some attrition in the
middle because of missing data on coal shipments in the 1920s. The pattern of
available series is the following:

1884:1-1884:5
1884:6-1887:12
1888:1-1889:12
1890:1-1893:12
1894:1-1922:3
1924:1-1926:12
1927:1-1935:12
1936:1-1937:12
1938:1-1940:12

Sugar, Wool, Coke and Pig Iron are missing.
Sugar, Wool, and Coke are missing.
Sugar and Coke are missing.
Coke is missing.
All series are available.
Coal is missing.
All series are available.
Coke is missing.
Coke and Sugar are missing.

To deal with missing series we do the following. First, we recalculate
the percentage weights, setting to zero the weight for the missing series.
Second, to prevent discontinuous jumps in the series, we ratio splice the
series whenever there is a change in coverage. Specifically, we take the
index for 1894:1-1923:12, for which all the series exist, as the base series.
Then, on either end, we ratio splice the less complete series to it. For
example, for 1890:1-1893:12 no data on coke shipments exist. Therefore, we
derive new weights and construct this less complete series through 1894:1.
Then we multiply this less complete series by the ratio of the complete series
in 1894:1 to the less complete series in 1894:1. This procedure is done
repeatedly, working out chronologically from the complete series.
The one complication to this procedure arises for coal for which there
is a gap in the data in 1924:1-1926:12. Since we have data for all series for
several years after 1926, it seems reasonable to use this complete series
unspliced. Therefore, what we do is ratio splice the less complete series to
the complete series in 1923:12, but then use no splice in 1926:12. This
should be fine, unless the trend of coal production over the missing years is
very different from that of other series.

Adjustment for Tariff Effects on Wool Receipts

For most purposes, it is desirable to use our index in its least
adulterated form. However, our index has a dramatic spike in mid-1897 that is
due to changes in the tariff on the wool receipts that we use to proxy for
wool textile production. This spike is sufficiently peculiar that it seems
reasonable to present an alternative index for part of this year. To do this,
we do not use the wool receipts series for the six months around the definite
spike in wool receipts (1897:2-1897:7). In making this adjustment, we ratio
splice the series excluding wool receipts to the complete series in 1897:8 but
then use the complete series before 1897:2 without a splice. This ensures
that the alternative only changes the observations for 1897:2-7, not all
earlier observations.

Final Indexes

INDXP
Miron-Romer Index of Industrial Production, 1909-100
Available 1884:1 - 1940:12

INDXPW
Miron-Romer Index with Adjustment for Wool Receipts, 1909-100
Available 1884:1 - 1940:12

Table A2 presents the new index in both its basic form and with the
minor adjustment for the effect of the tariff change on wool receipts.

REFERENCES

Babson, Roger W. (Various annual issues).
New York: Harper and Brothers.

Business Barometers and Investment.

Beaulieu, J. Joseph and Jeffrey Miron. (1989). "Seasonality in U.S.
Manufacturing." Unpublished manuscript, University of Michigan.
Calomiris, Charles and R. Glenn Hubbard. (1989). "Price Flexibility, Credit
Availability, and Economic Fluctuations: Evidence from the United
States, 1894-1909." Quarterly Journal of Economics 104 (August): 429452.
De Long, J. Bradford and Lawrence Summers. (1986). "The Changing Cyclical
Variability of Economic Activity in the United States." In The American
Business Cycle: Continuity and Change. Robert J. Gordon, ed. Chicago:
University of Chicago Press.
Dominguez, Kathryn M., Ray C. Fair, and Matthew D. Shapiro. (1988).
"Forecasting the Great Depression: Harvard Versus Yale." American
Economic Review 78 (September): 595-612.
Fabricant, Solomon. (1940). The Output of Manufacturing Industries. 18991937. New York: National Bureau of Economic Research.
Frickey, Edwin. (1947). Production in the United States: 1860-1914.
Cambridge: Harvard University Press.
Gordon, Robert J. (1982). "Price Inertia and Policy Ineffectiveness in the
United States, 1890-1980." Journal of Political Economy 90 (December):
1087-117.
Gorton, Gary. (1988). "Banking Panics and Business Cycles."
Papers 40: 751-81.

Oxford Economic

Macaulay, Frederick R. (1938). The Movement of Interest Rates. Bond Yields,
and Stock Prices in the United States Since 1856. New York: National
Bureau of Economic Research.
Miron, Jeffrey A. (1989). "The Founding of the Fed and the Destabilization of
the Post-1914 U.S. Economy." In A European Central Bank? Perspectives
on Monetary Unification after Ten Years of the E.M.S., Marcello de Cecco
and Alberto Giovannini, eds. Cambridge: Cambridge University Press.
Moore, Geoffrey H. (1961). Business Cycle Indicators.
Princeton University Press.
Persons, Warren M. (1931).
Sons.

Vol. II. Princeton:

Forecasting Business Cycles.

New York:

Wiley and

Romer, Christina. (1986a). "Spurious Volatility in Historical Unemployment
Data." Journal of Political Economy 94 (February): 1-37.

of the Data?"

American Economic Review 76 (June): 314-34.

. (1987). "Changes in the Cyclical Behavior of Individual
Production Series." NBER Working Paper No. 2440.
. (1989). "The Prewar Business Cycle Reconsidered: New Estimates of
Gross National Product, 1869-1908." Journal of Political Economy 97
(February): 1-37.
Schwert, G. William. (1988). "Why Does Stock Market Volatility Change Over
Time?" Manuscript, University of Rochester.
U.S. Board of Governors of the Federal Reserve System. (1986).
Production.
U.S. Bureau of the Census. (1975).

Zarnowitz, Victor. (1987).
Paper No. 2381.

Industrial

Historical Statistics of the United

"The Regularity of Business Cycles."

NBER Working

NOTES

1
This series is used by Dominguez, Fair, and Shapiro (1988) and Schwert
(1988) .
2
This series is used by Calomiris and Hubbard (1989) , Zarnowitz
(1987), and Gorton (1988).
3

This series is used by Gordon (1982).

4
For 1877-1902, the Persons Index is based only on bank clearing in
seven cities and pig iron production. After 1902 Persons's index includes
data on merchandise imports, railroad earnings, and employment, as well as
bank clearings and pig iron production (see Persons, 1931, pp. 1ll and 131).
5
Several of the series we use are available for at least part of the
period from the records of the NBER that are on a tape deposited at the Interuniversity Consortium for Political and Social Research. The Appendix
describes in detail how we use the data from the tape in our analysis.
6
This adjustment for production days is currently done by the Federal
Reserve in the derivation of its seasonally-unadjusted index of industrial
production.
7

See Romer (1986b) for a more thorough discussion of this effect.

8
The exact sources of the alternative series used are the following.
For the Babson index we use the version given in Moore (1961), pp. 130-131.
We ratio splice the two variants of the index in 1933:1. For the Persons
index we use the Index of Production and Trade given in Persons (1931), pp.
91-167. The pig iron series is from Macaulay (1938), Table 27, pp. A252-A270.
The FRB series is from the U.S. Board of Governors of the Federal Reserve
System (1986) , p. 303.
9
In addition to the annual average of the various monthly series, Table
1 shows the behavior of the widely-used Frickey series that is only available
on an annual basis. These data are from Frickey (1947), Table 6, p. 54.
10
Specifically, the FRB index in this period is based on approximately
80 series, whereas our index includes only 13.
11
It is also important to note that for the interwar period the FRB
index includes several employment series. If there is labor hoarding, then
employment will tend to be less volatile than output. This feature of the FRB
index could explain some of its relative stability.
12
The fact that the Persons series is more volatile in the prewar era
may be due to the interaction of the use of bank clearings data and the
frequency of financial panics in the era before 1914.

13
The standard deviation of our new seasonally-unadjusted index for
1922:1-1928:12 is 10.72. The fact that the interwar period is more volatile
than the pre-WWI period has been emphasized by Miron (1989). The results
presented here verify those findings with an independent source of data and
show that the same conclusion holds when monthly, as well as annual, variation
is considered.
14
The R2 of the regression of log growth rates on seasonal dummy
variables is .05 for the prewar pig iron series, .05 for the interwar pig iron
series, and .29 for the interwar FRB series.
15
See Beaulieu and Miron (1989) for an analysis of seasonality in
postwar manufacturing.
16
It is important to note that these differences in timing are not due
to the fact that our index is seasonally unadjusted. The same patterns emerge
when our series is adjusted using a regression against seasonal dummy
variables and a linear trend.

TABLE 1
STANDARD DEVIATIONS OF ALTERNATIVE MEASURES OF INDUSTRIAL PRODUCTION
(Growth Rates)
Monthly Data

1884:2-1940:12

1884:2-1913:12

1914:1-1940:12

Miron-Romer
Miron-Romer (SA)

9.17
9.00

7.05
6.44

11.07
10.88

Babson (SA)

3.16

2.54

3.64

Persons (SA)

2.92

3.03

2.73

Pig Iron
Pig Iron (SA)

9.33
9.13

7.86
7.68

10.73
10.46

NA
NA

4.40
3.71

Series

FRB
FRB (SA)

Annual Data

1885-1940

1885-1913

Miron-Romer

12.91

8.96

16.30

Babson

10.86

8.23

12.84

Persons

9.61

8.67

11.25

Pig Iron

29.33

19.53

37.26

FRB

16.03

Frickey

10.04

Series

1914-1940

Notes: (SA) denotes seasonally adjusted. The seasonally-adjusted results for
the Miron-Romer, pig iron, and FRB series are based on the residuals from a
regression of the growth rate on seasonal dummies. Because of data
unavailability, the calculations for the Babson index begin in 1889:2, those
for the Persons index end in 1930:12, and those for the FRB index begin in
1919:2. The Frickey index is available only on an annual basis. The growth
rates reported in the monthly section of the table are measured at monthly
rates; those reported in the annual section are measured at annual rates.
Sources: See the text for a description of the sources of the alternative
series and the derivation of the Miron-Romer index.

TABLE Al
VALUE-ADDED WEIGHTS

Value Added in 1909
Millions of Current $

Weights

Pig Iron Capacity

399.00

.3191

Anthracite Coal Shipments

124.05

.0992

Crude Petroleum Production

48.10

.0385

Sugar Meltings

31.60

.0253

Cattle Receipts

67.73

.0542

Live Hog Receipts

97.07

.0776

Coke Shipments

31.70

.0254

Flour Shipments

116.00

.0928

Wool Receipts

148.60

.1189

Coffee Imports

27.30

.0218

Tin Imports

26.70

.0213

Rubber Imports

74.60

.0597

Silk Imports

57.76

.0462

Series

Sources:
1909.

See text for a description of the sources of data on value added in

TABLE A2
MIRON-ROMER INDEX OF INDUSTRIAL PRODUCTION, 1884-1940
(1909 = 100)

1884:
1884:
1885:
1885:
1886:
1886:
1887:
1887:
1888:
1888:
1889:
1889:
1890:
1890:
1891:
1891:
1892:
1892:
1893:
1893:
1894:
1894:
1895:
1895:
1896:
1896:
1897:
1897:
1898:
1898:
1899:
1899:
1900:
1900:
1901:
1901:
1902:
1902:
1903:
1903:
1904:
1904:
1905:
1905:
1906:
1906:
1907:
1907:
1908:
1908:
1909:
1909:
1910:
1910:
1911:
1911:
1912:
1912:

1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
•1

7
1
7
1
7
1
7
1

7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7
1
7

32.663
30.341
35.326
30.918
39.204
38.719
39.015
36.082
39.480
43.737
42.128
51.524
49.559
56.701
55.409
57.242
60.392
61.492
55.857
51.272
46.837
46.403
54.305
65.404
63.328
54.103
55.224
68.310
63.100
59.430
68.140
74.476
73.861
64.911
69.643
80.838
81.893
73.900
83.149
87.148
74.276
78.118
93.714
86.292
93.360
91.710
95.781
99.264
73.117
80.613
91.660
99.357
109.875
93.689
92.979
100.463
102.967
115.469

29.
33. 431
31. 642
31. 070
37. 329
38. 032
39. 624
39. 705
38. 074
42. 688
44. 953
44. 540
46. 493
55. 276
54. 166
55. 288
59. 833
60. 742
56. 001
44. 202
44. 536
59. 769
59. 508
68. 056
57. 532
52. 547
65..800
64. 597
68..985
60..674
66..896
74. 931
75..717
68. 214
71..232
86..739
78..653
83..801
84..154

93.639
.555
86.334
85.
96..829

93.886
.550
99.239
96.470
97
105 .889
75 .710
85 .614
99 .906
99 .782

111.897
.562
104
96 .906
100 .479

111.696
.931
121

.329

27.334
31.086
31.372
33.523
36.119
41.653
39.667
41.553
40.647
45.294
41.061
45.283
47.928
54.878
56.316
56.872
59.295
58.238
55.670
42.720
46.241
57.723
56.091
62.433
58.877
52.593
66.613
66.984
62.222
63.750
63.664
74.161
74.329
65.118
78.872
81.543
81.498
76.571
85.724
87.414
81.760
72.676
92.465
89.297
91.993
96.620
96.950
97.179
74.413
80.305
97.895
101.209
105.637
93.698
95.179
96.579
112.279
113.269

32.
39. 417
33. 220
43. 941
37. 127
43. 557
38. 643
47. 546
39. 254
48. 423
41. 058
53. 480
48. 171
58. 316
48. 035
64.819
469
56.
60. 494
58. 800
48. 165
50. 753
61..988
56. 238

71.535
.810
55.
60.,293

84.313
,533
69.954
59.078
66.
63..165
75..411
75..746
61..783

33.
41. 379
34. 070
47. 757
39. 974
47. 065
38. 502
48. 505
39. 023
48. 557
44. 079
53. 462
50. 015
61. 961
47. 391
69. 600
58. 095
61. 457
55. 882
47. 306
48. 575
63. 067
58. 783
71. 480
57. 750
61. 166
70. 256
71. 553
63.879
567
70.
67. 770

32.
42. 529
34.386
527

77.117
.595

74.992
.302

69.

68.
68..664

42.973
42.485
45.
37. 893
43. 058
40. 236
47. 647
46. 629
50. 024
53. 542
51. 711
51. 690
64. 910
62. 993
57. 885
53. 369
48. 950
54. 109
54. 994
61. 676
66. 422
57. 012
66.485
992
69.

67.950
.415
58.

70.569
,679
73.

75.537
.941

67.613
,295
80.885

85,

81.721
.826
76.403
84.833
73.288

73.539
.057

77
84 .899
93 .970

81,

95.278
.843

93.436
.031

87
86 .263
97 .404
93 .103

88.810
.880

83
95 .824
97 .191
93 .378
70 .398
86 .207
90 .419
108 .816
92 .008
98 .770
95 .192
102 .804
101 .223
114 .905

.721

86.
83..707

75.042
.485
89.464
.020
39

80.181
.823
81.344
74.
84..498

89.291
.516
72.568

96
91 .383
100 .739
100 .462
114 .992

97
97 .484
71 .813
79 .268
94 .211
99 .622
106 .274
93 .841
91 .088
94 .205
99 .220
107 .965
114 .427

.222

.916

96.123
.996
95
81 .414
75 .228
96 .243
94 .892

111.270
.327
90.781

TABLE A2 (CONTINUED)
1913:1
1913:7
1914:1
1914:
1915: 7
1915: 1
1916: 7
1916: 1
1917: 7
1917: 1
1918: 7
1918: 1
1919: 7
1919: 1
1920: 7
1920: 1
1921: 7
1921: 1
1922: 7
1922: 1
1923: 7
1923: 1
1924: 7
1924: 1
1925: 7
1925: 1
1926: 7
1926: 1
1927: 7
1927: 1
1928:1
7
1928:
1929: 7
1929: 1
1930: 7
1930: 1
1931: 7
1931: 1
1932: 7
1932: 1
1933: 7
1933: 1
1934: 7
1934: 1
1935: 7
1935: 1
1936: 7
1936: 1
1937: 7
1937: 1
1938: 7
1938: 1
1939: 7
1939: 1
1940: 7
1940: 1
7

118.687
109.059
98.734
109.263
95.962
122.857
158.881
131.197
167.560
150.507
138.945
177.825
142.696
183.409
188.548
160.519
123.392
108.381
152.772
171.185
213.366
183.047
174.347
141.532
211.625
189.792
231.678
210.252
223.671
213.196
210.119
193.207
263.791
237.105
223.833
201.930
176.986
202.097
152.278
145.559
129.017
221.452
184.349
187.069
164.291
198.033
164.985
198.554
215.962
200.692
177.320
130.150
172.516
179.191
275.583
282.412

117.834
115.755
108.798
98.375
112.322
124.795
161.200
140.186
138.983
168.540
148.571
152.389
149.133
147.749
200.361
163.336
131.139
119.869
176.200
150.457
207.595
164.666
203.993
146.605
191.158
192.751
215.503
184.094
190.618
201.001
203.581
190.626
294.849
221.448
226.560
202.824
185.612
179.401
140.377
147.121
113.479
210.920
164.288
140.726
201.865
172.670
173.532
194.232
232.051
220.668
171.663
149.443
156.213
179.728
200.597
285.488

112.744
116.286
115.228
103.849
122.909
129.052
154.670
129.589
159.420
159.861
151.569
161.296
175.749
158.924
205.850
134.146
130.893
117.457
169.564
141.971
219.442
136.006
171.978
161.537
207.232
179.293
226.918
208.725
202.582
189.340
213.080
211.114
241.635
217.829
218.751
184.965
192.290
172.174
167.411
141.451
115.877
207.182
170.521
150.742
184.372
173.749
162.884
213.904
207.278
234.997
161.271
157.510
182.798
192.803
232.237
320.230

108.121
108.856
117.876
95.612
123.367
134.790
148.696
141.590
167.068
157.079
159.383
151.073
174.088
151.000
184.287
124.872
123.679
133.106
137.692
202.040
218.093
159.379
207.044
192.922
189.900
198.567
211.027
195.042
232.712
187.029
214.013
223.226
255.798
215.283
219.995
210.960
197.385
180.480
153.193
153.656
109.910
190.378
187.549
134.765
171.052
171.206
197.327
193.485
216.205
221.343
140.976
164.739
158.482
215.692
262.499
304.910

106.299
107.716
112.619
90.894
126.742
145.126
152.434
143.638
169.931
158.831
181.297
152.910
165.780
184.366
155.123
142.416
106.174
143.169
134.225
185.052
220.596
156.197
166.825
201.284
194.892
215.119
192.517
219.704
212.356
200.560
197.230
206.807
246.418
222.017
209.518
167.043
179.835
198.151
141.321
135.835
128.269
170.434
197.562
159.877
146.230
145.481
178.358
202.694
223.829
215.345
138.488
167.273
184.297
207.562
221.192
309.145

111.631
107.199
107.625
92.329
119.807
156.137
153.823
151.944
180.591
151.446
165.793
146.861
151.116
183.408
165.860
121.442
114.304
151.867
157.703
211.121
218.777
189.616
155.102
186.075
185.285
220.923
178.711
207.721
209.936
177.709
190.558
224.832
240.253
221.397
223.314
182.274
210.721
201.333
159.795
136.726
151.576
162.880
202.631
100.768
170.133
180.875
194.893
227.087
232.285
231.944
139.900
167.767
172.813
281.021
252.392
372.697

Index Excluding Wool Receipts for 1897:2-1897:7

1897: 1
1897: 7

55.224
60.042

60.798
64.985

57.812
66.984

59.811
69.954

63.079
71.567

63.925
67.950

Sources: See the text for a description of the data and
procedures used to derive the new Miron-Romer index.

FIGURE 1
ALTERNATIVE MEASURES OF INDUSTRIAL PRODUCTION
Logarithms

Notes: To improved the clarity of the figure, we have added 1.5 to the
logarithm of the Persons index in all years.
Sources: See the text for the sources of the Babson and Persons series and
for a description of the derivation of the new Miron-Romer index.

FIGURE 2
ALTERNATIVE MEASURES OF INDUSTRIAL PRODUCTION
Logarithms

Notes: To improve the c l a r i t y of the figure we have subtracted .15 from the
logarithm of the Fig Iron s e r i e s in a l l y e a r s .
Sources: See the t e x t for the sources of the Pig Iron and Federal Reserve Board
s e r i e s and for a d e s c r i p t i o n of the d e r i v a t i o n of the new Miron-Romer index.

Full text of A New Monthly Index of Industrial Production, 1884-1940

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