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orjcmg raper series
Business Cycle Durations and Postwar
Stabilization of the U.S. Economy
M a rk W . W a ts o n
W o rk in g P a p e r s S e r i e s
I s s u e s in M a c r o e c o n o m i c s
R e s e a r c h D e p a r tm e n t
F e d e r a l R e s e r v e B a n k o f C h ic a g o
April 1 9 9 2 (W P -9 2 - 6 )
F E D E R A L
O F
R E S E R V E
C H I C A G O
B A N K
Business Cycle Durations and Postwar Stabilization of the U.S. Economy
by
Mark W. Watson
Department of Economics
Northwestern University
Evanston, IL 60208
and Chicago Federal Reserve Bank
October 1991
(Revised January 1992)
This paper is an extension of my discussion of Diebold and Rudebusch (1991), presented at the
N BER Econom ics Fluctuations meeting in July 1991, and I thank the authors for stimulating my
interest in this area. I would also like to thank Robert Gordon, Robert King, Jeff Miron,
Glenn Rudebusch, and colleagues at the Chicago Federal Reserve Bank for useful comments and
suggestions. Special thanks go to Jim Stock for detailed suggestions, to Robert Gordon, Jeff
Miron and Christina Romer for making data available, and to Edwin Denson for excellent
research assistance. This work was supported by National Science Foundation grant no. SES89-10601.
Business Cycle Durations and Postwar Stabilization of the U.S. Economy
Abstract
The average length of business cycle contractions in the United States fell
from 20.5 months in the prewar period to 10.7 months in the postwar period.
Similarly, the average length of business cycle expansions rose from 25.3
months in the prewar period to 49.9 months in the postwar period. This paper
investigates three explanations for this apparent duration stabilization. The
first explanation is that shocks to the economy have been smaller in the
postwar period. This implies that duration stabilization should be present in
both aggregate and sectoral output data. The second explanation is that the
composition of output has shifted from sectors that are very cyclical, like
manufacturing, to sectors that are less cyclical, like services. This would
lead to increased stability in aggregate output even in the absence of
increased stability in the individual sectors. The third explanation is that
the apparent stabilization is largely spurious, and is caused by differences
in the way that prewar and postwar business cycle reference dates were chosen
by the NBER. The evidence presented in this paper favors this third
explanation.
JEL Classification numbers: N10, E32
1. Introduction
A key piece of evidence supporting the efficacy of aggregate demand management is the
observation that, on average, postwar business cycles in the United States have been less severe
than in the prewar period. This argument, presented by Burns (1960) and subsequently
investigated by other researchers, has been seriously challenged in a series of papers by Romer
(1986a, 1986b, 1989,1991).* Romer’s argument is that the apparent stability of the postwar
economy is largely an artifact of measurement error in the prewar data, which spuriously
increases its volatility. However, much of the evidence supporting the contention of postwar
stabilization has not relied on the volatility in specific series, but instead on the duration of
business cycles calculated using the historical reference dates determined by researchers at the
National Bureau of Econom ic Research (NBER). These duration data suggest that the average
length of recessions has fallen dramatically in the postwar period: from 1854-1929 contractions
averaged 20.5 months, while from 1945-1990 they averaged 10.7 months; similarly over the
same periods, prewar expansions averaged 25.3 months, while postwar expansions averaged 49.9
months. Diebold and Rudebusch (1991) show that these prewar-postwar differences are
statistically highly significant and robust to many of the changes in NBER business cycle
chronology debated in the historical literature.
This paper investigates three explanations for this apparent stabilization of the postwar
economy. The first explanation is that shocks to individual sectors of the econom y were smaller
in the postwar period than in the prewar period. This may reflect a fortuitous exogenous
change in the process generating shocks, or it may reflect effective government policy
dampening the effects of exogenous shocks. The empirical analysis reported below offers little
support for this explanation.
The second explanation is that the cyclical behavior of individual sectors was the same in
the prewar and postwar periods, but that changes in the relative importance of the sectors led to
-1 -
changes in the cyclical behavior of the aggregate economy. For example, the service sector has
traditionally been less cyclical than the manufacturing sector, and over time has grown in
importance relative to the manufacturing sector. Once again, the empirical analysis offered
below does not support this explanation.
The third explanation is that the differences in durations are spurious, caused by systematic
biases in the information used to form the reference dates. The empirical analysis presented in
this paper supports this explanation. In particular, the analysis suggests that the paucity of
prewar data forced early NBER researchers to focus their attention on a small number of
econom ic time series, and these series represent sectors of the econom y that are systematically
more volatile than aggregate activity. This exaggerated volatility reflected itself in longer
contractions and shorter expansions in the prewar period.
The remainder of this paper presents evidence on the relative plausibility of these three
explanations. In Section 2, contraction and expansion durations in "specific cycles" of
individual series are investigated to see if these have changed across the prewar and postwar
period. Little evidence of change is found in the individual series. Section 3, which
investigates the effect of the changing composition of output on the durations of the business
cycle, suggests that changing composition cannot explain the differences between the prewar
and postwar durations. Section 4 reviews the construction of the prewar reference dates and
compares the data used to date prewar business cycles with the data used to date postwar cycles.
This analysis suggests that the prewar business cycle chronology relied on data with a much
narrower focus than the data used to date postwar cycles. When postwar cycles are dated using
data similar to that used to date prewar cycles, little difference between the prewar and postwar
periods is evident. Some concluding comments are offered in the final section.
-2-
2. Phase Durations of Specific Series
The questions raised in the introduction can only be resolved by comparing prewar and
postwar data, and as Romer’s work shows, extreme care must be exercised in such a
comparison: the series used must be of consistent quality (either good or bad) across the prewar
and postwar period. Data availability enforces a tradeoff between coverage and sampling
interval. The available annual data cover many sectors of the economy, but are far from ideal
for business cycle analysis, since annual data can mask short or mild contractions. Monthly
data are more useful, but there are few monthly series of consistent quality spanning a
significant portion of the prewar and postwar period. In this section both monthly and annual
data are used to uncover prewar-postwar changes in the average phase durations of "specific
cycles" associated with these series.
Identifying specific cycles in economic time series requires a precise definition of a
"contraction” and an "expansion." Unfortunately, the definition of contraction and expansion
used by the NBER is too vague for this purpose.
2
This paper uses an objective definition
embedded in an algorithm developed by Bry and Boschan (1973).
3
This algorithm is a set of ad
hoc filters and rules that determine business cycle turning points in an econom ic time series.
Essentially, the algorithm isolates local minima and maxima in a time series, subject to
constraints on both the length and amplitude of expansions and contractions. For many series,
the Bry-Boschan algorithm does a remarkably good job at reproducing the turning points
selected by "experts." For example, Chart 1 shows monthly values of pig iron production from
1877-1929. The horizontal lines on the graph are the turning points selected by the BryBoschan procedure; the arrows point to the turning points selected by Burns and M itchell
(1946).
Little difference between the Bry-Boschan and Burns and Mitchell peaks and troughs
is evident.
Consistent with practice at the NBER, the Bry-Boschan algorithm dates contractions and
expansions using the level of the series, rather than the detrended series. Thus, contractions
-3-
correspond to sequences of absolute declines in a series, and not to periods of slow growth
relative to trend: the algorithm dates "business cycles" and not "growth cycles." This will be
important when interpreting the changes in prewar and postwar average phase durations for
series that experienced a significant change in their trend rate of growth. Changes in trend
rates of growth have obvious effects on contraction and expansion lengths: decreases in average
growth rates lead to increases in average contraction duration and decreases in average
expansion duration.
Table 1 shows average phase durations calculated using the Bry-Boschan dating algorithm
for a variety of monthly prewar and postwar series. For many of these series the prewar and
postwar data are not perfectly comparable, and comparisons using a variety of postwar series are
presented. To eliminate any effect of the Great Depression, the prewar period is truncated in
1929, although the substantive conclusions offered below are unaltered if the period is extended
to 1940. For each series the table presents the average length of contractions (Q, and the
average length of expansions
in the prewar and post war period. As discussed above, since
contractions are defined as absolute declines, rather than declines below trend growth, average
annual growth rates (X = sample mean of (log(xt) - lo g t x ^ ) ) ) over the two sample periods
are shown, as are the t-statistic for testing the null hypothesis of no change in the growth rate
(t^j). In addition, the standard deviation of the annual growth rates (a) for the prewar and
postwar periods are shown together with the ratio of prewar to postwar standard deviations.
Finally, the table presents the W ilcoxon rank sum statistic (Wc and W g) for comparing the
prewar and postwar contraction and expansion phase durations. The statistic is presented in
standardized form and can be interpreted like a t-statistic for a significant change in the
average duration.^ (For example, absolute values greater than 2 are statistically significant.)
A serious problem with the monthly data is that there are few direct indicators of output or
employment; many of the series are from the financial sector and are imperfect indicators of
sectoral or aggregate output. The first panel of the table shows results for a variety of financial
- 4 -
series. In summary, real stock prices show little change in cyclical behavior, long term interest
rates show a slight increase in the length of postwar expansions with little change in the length
of contractions, and commercial paper rates show a decrease in postwar contraction duration and
increase in expansion duration.^ For these series the only statistically significant change is for
commercial paper expansions. The other financial series - business failures, stock exchange
volume and bank clearings - show large changes in trend growth rates, which makes it
difficult to compare the prewar and postwar duration average durations.
Panel B presents results for production indicators. The first set of comparisons involve
prewar pig iron production and postwar industrial production indices for metals and steel. In
the postwar period, contractions are longer and expansions shorter, but this reduction is
undoubtedly related in part to the decline in the growth rate of this sector. The next
comparison involves prewar railroad freight ton-miles and postwar manufacturers shipments.
Again, the rapid growth in the prewar period makes this comparison difficult. The final, and
potentially most informative comparison, involves the prewar industrial production index
constructed in Miron and Romer (1990) and postwar industrial production indices. Comparing
the Miron-Romer series to postwar aggregate index of industrial production suggests results
very similar to the NBER phase durations for expansions: postwar expansions are roughly twice
as long as prewar expansions. While this comparison is tempting, it turns out to be
inappropriate because the mix of goods in the Miron-Romer series differs significantly from the
mix of goods in the aggregate IP index. To control for the mix of goods in the index, the final
row of the table compares the Miron-Romer index to a postwar index with approximately the
same product mix.
This postwar index has remarkably similar cyclical properties as the
Miron-Romer index. Panel C contains two comparisons involving prices and construction.
Prices are difficult to compare because of changes in trend behavior. There appears to be little
change in the average contraction and expansion durations for construction.
Table 2 presents results for annual data. The complications of the Bry-Boschan dating
algorithm are not needed for annual data: contractions are defined as sequences of absolute
-5-
declines in the series, and expansions are defined as sequences of absolute increases. The top
panel of the table shows results for various measures of aggregate activity. The first two rows
are for the GNP series constructed by Romer (1989) and by Balke and Gordon (1989). Both
series show a slight decrease in the average length of contractions in the postwar period; for
Romer’s series average contraction duration decreases by .1 years and for the Balke-Gordon
series by 3 years in the postwar period. For both series, average postwar expansions are
shorter than their prewar counterparts. The Romer and Balke-Gordon prewar series are based
in large part on Shaw’s (1947) commodity output series. This series accounts for between one
third and one half of real GNP during the prewar period. The results for Shaw’s commodity
output are shown in row 3 of the table. The results for this series are similar to those for GNP:
postwar contractions and expansions are slightly shorter than their prewar counterparts. The
next row presents results for annual industrial production: the prewar data is the series
constructed by Frickey (1947) and the postwar data is Romer’s (1986b) extension of Frickey’s
series. This series shows a significant break in trend, making comparison of the postwar and
prewar phase durations difficult. Finally, the last row in the panel considers Romer’s (1986a)
unemployment data. For this series, postwar contractions are shorter and expansions longer
than in the prewar, but the differences are not great: .2 years for contractions, .1 years for
expansions. In summary the aggregate annual data show little evidence of dramatic differences
in prewar and postwar cyclical behavior.
Panels B-D present results for individual sectors of the economy. The series are from
Romer (1991a), and they were chosen because they satisfy the requirement of consistent quality
O
through the entire sample period. The results for these series are summarized in the bottom
panel of the table, which compares the frequencies of series for which average expansion and
contraction lengths increased in the postwar period to the frequencies for which they decreased.
These are presented for series without significant changes in trend growth rates. The summary
table suggests that there is a tendency for postwar contractions to be longer than prewar
contractions, and for postwar expansions to be shorter than prewar expansions.
- 6 -
Taken together, the annual data provide little support for the notion that contractions are
shorter and expansions longer in the postwar period. An important caveat is, that while this
may accurately reflect the cyclical behavior of these series, it may also reflect the limitations of
using annual data to analyze phase durations.
Three conclusions emerge from these data. First, they suggest that there has been little
change in the average phase durations of sectoral output. This is evident from the annual data,
in which many sectors were considered, and in the monthly data which considered pig iron
production and a fixed weight index of industrial production. The second conclusion is that
these results carry over to aggregate series also. This is evident from the annual data on GNP
and unemployment and the monthly data on stock prices. Third, while average phase durations
do not seem to have changed, there is evidence that volatility has decreased. This is easily seen
in Table 2, which presents the ratio of the standard deviation for growth rates for prewar and
postwar data. A reduction in variability can also be seen for many of the monthly series.
These conclusions are tempered by three caveats: first, the monthly data are very limited;
second, the annual sectoral data represent production of commodities and there is no data on the
other sectors of the economy; finally, the prewar annual GNP series are less representative of
the aggregate economy than the postwar series because of measurement problems documented in
Balke and Gordon (1989) and Romer (1989).3
3. Sectoral Changes
One potential explanation for the reduced cyclicality in the postwar period is the changing
composition of aggregate output. This explanation is discussed in some detail in Zarnowitz and
Moore (1986), who document the increasing importance of "less cyclical” relative to "more
cyclical” sectors in the postwar period. Table 3 presents some related evidence. It shows the
historical evolution of sectoral shares of total employment together with postwar average phase
durations of sectoral employment. During the postwar period, the more cyclical sectors -
-7-
manufacturing, transportation, communications and public utilities, and mining - grew more
slowly than the less cyclical sectors. Most notable is the share of manufacturing (a highly
cyclical industry) which fell from 34.7% of nonagricultural employment in 1948 to 17.3% in
1990, and the share of service employment (a very noncyclical sector), which rose from 11.5%
in 1948 to 25.6% in 1990. This increase in the share of employment in noncyclical sectors
suggests a reduction in the cyclicality of aggregate employment, even in the absence of changes
in the individual sectors.
However, some of the data in the table suggests that sectoral composition may not explain
the differences between the prewar and postwar periods. In particular, the table shows that
growth rates within sectors have changed significantly through time. For example,
manufacturing employment grew at an average rate of 2.4% from 1869-1929, and only 0.4%
from 1948-1990. Since downturns are measured as absolute declines in a series and not declines
relative to trend, this suggests that manufacturing was less cyclical in the prewar period (when
it had a larger trend) than in the postwar period (when it had a smaller trend). Thus, even
though manufacturing accounted for a smaller share of aggregate employment in the postwar
period it may have been more cyclical. Thus, the trends in sectoral employment paint an
ambiguous picture of aggregate cyclicality in the prewar and postwar data.
If monthly prewar employment data were available, it would be possible to model changes in
stochastic process governing sectoral employment across the prewar and postwar periods, and to
deduce implications for the changing cyclical properties of aggregate employment.
Unfortunately, the only reliable prewar sectoral employment data are from the decennial census.
These data can be used to estimate trends, but by themselves provide little information about
the cyclical properties of the series. This makes it impossible to identify all of the prewarpostwar changes in the sectoral employment processes that affect the cyclicality in aggregate
employment.
However, since the trends in the prewar data can be estimated, it is possible to determine
how the differences in prewar and postwar employment trends affect the average phase
-8-
durations. This can be done by assuming that the only difference in the stochastic process
generating the prewar and postwar data is the difference in trend behavior. This is an extreme,
but useful assumption, since it allows the analysis to focus on the cyclical impact of the
employment trends shown in table 3. The assumption will be relaxed below, when other
potential changes in sectoral employment are investigated.
The cyclical impact of the trends in the data can be determined as follows. First, for each
sector, models for the trend are estimated for both the prewar and postwar periods. The
prewar trend model is estimated using the decennial census data, and the postwar trend model is
estimated using monthly data available from the BLS Establishment Survey. The postwar
monthly data are then detrended, and the resulting series is used to estimate a model for the
shortrun variability and interaction of the sectoral data. This shortrun model is then appended
to the estimated prewar model for the trends to produce a model for the prewar monthly data.
Thus, the prewar and postwar models differ only in their implications for the trend behavior of
the data; they share the same model for shorter run movements in the data. The cyclical
properties of the resulting employment series from the prewar and postwar models can then be
deduced.
The results from implementing this procedure are shown in Tables 4a and 4b. In table 4a,
the trends for each sector are estimated by regressing the logs of the data on a constant and
time trend. The prewar regressions used decennial data from 1869-1929 and the postwar
regressions used monthly data from 1948-1990. In table 4b, more flexible trends were
estimated by allowing for kinks in the trend line every twenty years; in the prewar period kinks
were allowed in 1889 and 1909 and in the postwar period a kink was allowed in 1968:1. In
both cases the shortrun model was estimated as a VAR(4) using the detrended logarithms of the
monthly postwar data. The resulting prewar and postwar models were then used to generate
pseudo monthly employment data for the 1869-1929 and 1948-1990 periods, the Bry-Boschan
algorithm was used to date business cycles in the resulting aggregate employment data, and
-9-
average contraction and expansion lengths were calculated for the realizations. This data
generation process was repeated 500 times and the resulting average phase durations are
reported in the table.
The results suggest three observations. First, there is very little difference between the
average phase durations across the two tables: the results from the broken trend model are very
similar to the fixed trend model. Second, the generated postwar data have average phase
durations very similar to the actual postwar aggregate employment data, and these in turn are
similar to the average phase durations of NBER dated business cycles. Third, the generated
prewar data have average contraction lengths similar to the postwar data and average expansion
lengths over one year longer than the postwar data. This suggests that the underlying trend
behavior in the sectoral employment data would be expected to lead to less cyclical behavior in
the prewar period than in the postwar period.
These observations are reinforced by percentiles of the empirical distributions from the 500
replications corresponding to the prewar and postwar NBER dated business cycles. These
percentiles are shown in parentheses in the tables. For example, looking at the postwar phase
durations, 14 percent of the realizations from the postwar single trend model had average
contraction lengths less than 10.7 months (the average duration of NBER dated postwar
contractions), and 38 percent of the realizations had average expansion lengths shorter than 49.9
months (the average duration of postwar NBER dated expansions). The percentiles in the table
indicate that the average phase durations for the postwar NBER dated business cycles are
consistent with the trend-VAR model used to generate the data, but that the N BER prewar
dated business cycles are not consistent with the model.
Thus far, the analysis has focused on trends, but other characteristics of the sectoral
employment stochastic process may explain the differences in the prewar and postwar phase
durations. For example, shocks may have been more highly correlated across sectors in the
prewar period. This would tend to increase the variance in the aggregate em ploym ent series
- 1 0 -
and potentially make it more cyclical.
g
Alternatively, sectoral employment may have been more
volatile in the prewar period. Unfortunately, since high frequency prewar employment data are
not available, it is impossible to statistically investigate these potential explanations. However,
it is possible to experiment with modifications of the model characterizing the postwar data, for
example doubling the correlation between the shocks, to find out what kinds of modifications
are required to explain the prewar phase durations.
While the VAR(4) fits the data well, it is not well suited for these experiments since it
allows complicated dynamic interaction between the eight sectors. This makes it difficult to
isolate the characteristics of the process which are responsible for the phase durations. Instead
of using V AR , the experiments are carried out using a dynamic factor model of the form
(5.1) x[ = a jft + uj
(5.2) f { = <£]ft_i + ^ t - 2 + e t
(53) uj = pju}^ + e[,
where xj is the detrended level of log employment in the i’th sector at time t, f t is a scalar
"common factor," e( and ej are zero mean white nose processes with variances a^ and
respectively, and E(et e^.)=E(£je^)=0 for all i, t, r and i^j. In this model, all of the
dynamic interaction in the sectors comes through the common factor fj. The "uniquenesses,”
uj, are uncorrelated across sectors and allow each sector to move independently of the other
sectors.
This model was fit to the detrended postwar data and the results are shown in Table 5a.
The results look sensible. The most cyclical sectors - mining construction and manufacturing
- have the largest values of a, indicating the largest amount of covariation. The least cyclical
sectors - government, services and F.I.R.E. - have the smallest values of a. The common
factor, f t, and each of the uniqueness, uj, are highly persistent with exact or near unit
autoregressive roots.^
-11-
Pseudo prewar and postwar data were generated by appending the dynamic factor model
onto the models for the prewar and postwar trends.^ The results from 500 realizations of the
processes are shown in the first two rows of Table 4b. This model produces data with average
contraction lengths similar to the trend-VAR model, but with somewhat longer average
expansions. The remaining rows of the table show results for modifications of the dynamic
factor model. For example, in the third row of the table, the model was modified by
multiplying each of the factor loadings by
J and reducing the variance of the uniquenesses by
l
an offsetting amount. This doubles the correlation between the sectors while leaving the
variance of each sector unchanged. This modification lengthens average contraction and
shortens average expansions, but not nearly enough to explain the prewar NBER data. In the
next three rows the factor loadings are increased by varying amounts and the uniqueness
variances are unaltered. The results suggest that a dramatic increase in the covariance of the
sectors is necessary to explain the results: the factor loadings need to be increased by a factor of
5, which corresponds to an increase in the covariance of the sectors by a factor of 25. This
modification has a dramatic effect on the variability of the data - the standard deviation of
the annual growth rate in the aggregate pseudo prewar data is over 5 times larger than the
postwar data.
This section suggests two conclusions about the effect of changes in the composition of
employment on prewar and postwar cyclicality. First, differences in the trend rate of growth
across sectors do not explain the differences in the prewar and postwar average phase durations.
Second, very dramatic, and implausibly large changes in the covariance structure of the prewar
and postwar employment data are necessary to explain the prewar average phase durations.4
4. Biases in the Prewar Data
The results from sections 2 and 3 suggest that there is little in the data to support the claim
that the postwar period has witnessed a reduction in the duration of cyclical contractions and an
increase in the duration of cyclical expansions. Why is such a change evident in the NBER
-12-
business cycle chronology? One explanation is that NBER researchers chose the prewar
reference dates in a way that fundamentally differed from the way that the postwar reference
dates were chosen. Two possibilities suggest themselves. First, the relative paucity of prewar
data suggests that NBER researchers may have chosen reference dates for the prewar period
using data that were systematically more cyclically volatile than the aggregate economy, and as
more data became available, this defect was corrected in the postwar period. This would imply
that the apparent postwar stabilization is due to the changing composition of series used to date
the cycle; it is not due to changes in the cyclical behavior of individual series or to changes in
the composition of aggregate output or employment. The second possibility is that the prewar
data may have been processed differently than the postwar data. For example, the prewar may
have been detrended while the postwar data was not.
To investigate the merits of these possibilities it is useful to review the procedure that
NBER researchers used to determine the prewar reference dates.
12
The prewar chronology was
chosen judgementally based on both quantitative and qualitative information. The qualitative
inform ation consisted in large part of the "business annals" collected in Thorpe (1926). These
annals are a summary of contemporaneous reports that appeared in the business and popular
press; for the U.S. they cover the period 1790-1925. The Thorpe annals provided an initial set
of reference dates, which were then refined by examining available monthly, quarterly and
annual time series.
The quantity and quality of these data improved dramatically over the sample period
covered. For example, only 19 monthly or quarterly series were available in I860; 8 of these
were price series, 8 were financial variables and only 3 were related to production - hog
receipts in Chicago, cattle receipts in Chicago and shoe shipments from Boston. By 1930 the
availability of data had changed dramatically: 710 monthly and quarterly series were available
13
and 245 of these related to production and personal incomes . Aggregate employment and
production indices played no role in the dating of the early cycles. M onthly data on aggregate
-13-
nonagricultural employment did not become available until 1929, although an index of factory
employment extended back to 1914.
The earliest monthly index of industrial production used
by Burns and Mitchell extended back to 1904.^ Monthly and quarterly estimates of personal
income and gross and net national product did not exist for the pre-1920 period. Burns and
M itchell list 46 m onthly and quarterly series available before 1890.^ Of these 10 are indirect
indicators of business activity, such as the volume of bank clearings, 4 are orders for durable
goods or construction, 2 are production indicators, 15 are price indices or price series, 9 are
financial indicators such as stock prices and interest rates, and 4 are indicators of business
failures. Many of these series were included in the monthly indicators included in Table 1.
U nfortunately the historical record does not provide a detailed description of how Thorpe’s
qualitative data were combined with the available statistical data to determ ine the prewar
reference dates. Romer (1991b) provides a very useful summary of the historical record. She
has traced the pre-1927 reference dates back to an NBER news bulletin dated M arch 1,1929
that was apparently written by Mitchell, but the document contains little specific guidance
about how the dates were determined. On the other hand, Mitchell’s 1927 book, Business
Cycles: The Problem and its Setting, contains a detailed discussion of Thorpe’s Annals and the
available statistical data that could potentially be used for choosing reference dates.
Romer (1991b) points out that two time series, the A.T.T. business index and Snyder’s
clearing index, receive particular attention in the discussion in M itchell’s 1927 book. The
A.T.T. index begins in 1877, and is a combination of data series meant to measure general
business activity. From 1877 to 1884 it was based solely on pig iron production; bank clearing
outside New York City and blast furnace capacity were added in 1885 and wholesale prices
were added in 1892 (M itchell (1927), page 294). Snyder’s clearing index begins in 1875, and is
based on bank clearings outside New York City deflated by a price index. As stressed by
Romer, the key characteristic of both of these series is that they are presented as deviations
from trends, rather than levels. Thus, if these series influenced the choice of prew ar dates,
they could im part a "growth cycle" bias in the prewar business cycle chronology.
- 1 4 -
An estimate of the magnitude of this bias can be calculated by comparing the average phase
durations for levels and detrended levels of pig iron production and bank clearings, the most
im portant components of the A.T.T. business index and Snyder’s clearing index. The average
phase durations for the levels were given above in Table 1. As would be expected, the
detrended series had longer average contractions and shorter average expansions than the series
in levels. But the differences were not large. For pig iron production, the difference was 2.4
months for contractions (12.9 versus 15.3) and 4.9 months for expansions (28 versus 23.1). For
bank clearings, contractions were 2.8 months shorter (16.2 versus 1.34) and expansions 6
months longer (18.9 versus 24.9 months) using the detrended data.
17
To put these differences
into perspective, recall that the postwar contractions are an average of 9.8 m onths shorter than
prewar contractions and postwar expansions are an average of 24.6 months longer than prewar
expansions. Thus, while the use of the detrended A.T.T. business index and Snyder’s clearing
index may have biased the average phase durations, these biases are small compared to
differences in the prewar and postwar average phase durations.
An alternative explanation of the differences in the average prewar and postwar durations,
is that the data used to date the prewar cycles was systematically more volatile than aggregate
activity, and that this bias was eliminated in the postwar period. A simple way to investigate
this explanation is to date postwar business cycles using only those indicators that were used to
date the prewar cycles. That is, to "Romerize” the postwar reference dates by artificially
restricting the postwar data to be as limited as the prewar data.
Table 6 presents peak and trough dates for seven series covering the same range of activities
as the 46 series available to Burns and Mitchell. The notable deletions from the list is any
consideration of bank clearing and prices, because of the change in the drift in these series
shown in Table 1. Moreover, I have not attempted to construct a postwar annals, analogous to
that constructed by Thorpe.
18
Evident in the table is a clustering of "specific cycles" for the individual series, consistent
with the notion of the business cycle. While the Bry-Boschan algorithm determines turning
-15-
points in individual series, it does not solve the multivariate problem of determ ining a
"reference cycle" from a collection of series. Here, I have used judgement based on the turning
points in the individual series to construct a set of reference dates. These are shown in the
table along with the NBER reference dates. In selecting the reference dates, I assumed that the
two production indices were coincident indicators; that is, on average, they moved
contemporaneously with the cycle. W hen specific cycles in these series approxim ately
coincided, I averaged the peak and trough dates. For each production index there were specific
cycles that did not correspond with movements in other series, and these were ignored when
choosing the reference dates. Panel B of the table shows the reference dates that I selected
along with the lead-lag relations of the individual indicators. These suggest reasonable
conform ity across cycles.19
These pseudo reference dates suggest a much more volatile postwar period than the NBER
reference dates. They suggest three more recessions (1951:4-1952:1,1966:1-1967:6 and 1984:41986:1), longer contractions and shorter expansions.
20
Summary statistics comparing average
phase durations from these pseudo reference dates to the NBER prewar and postwar
chronologies are presented in panel C of the table. These data suggest little change in the
length of expansions across the prewar and postwar periods and a reduction in the length of
contractions that is only one half as great as suggested by the NBER chronology. Moreover,
neither of the changes is statistically significant.6
6. Concluding Remarks
This paper has investigated three explanations for the postwar duration stability evident in
the N BER business cycle chronology. Little support is found for explanations that lead to
duration stability across individual sectors of the economy: for most individual series, average
contraction and expansion durations for the prewar and postwar periods are similar. Thus,
there is little evidence that more effective demand management in the postwar period has led to
-1 6 -
increased duration stability. The data also cast doubt on the changing composition of output
and employment as the cause of the apparent postwar stability. Historical differences in trend
growth rates of sectoral employment explain little of the observed changes in average duration.
An explanation that is consistent with the data is that the prewar NBER business cycle
chronology was determined by data that, at least in the postwar period, are systematically more
volatile than the aggregate economy. Thus, selection bias in the data series available to
researchers in the prewar period appears to be the most likely explanation for the postwar
duration stability apparent in the NBER data.
Two points should be kept in mind when interpreting these conclusions. First, these results
concern only the duration of contractions and expansions and say nothing about their amplitude.
A more meaningful answer to the question of postwar stability may hinge on relative amplitudes
or volatilities rather than on durations. The data summarized in Tables 1 and 2 suggest an
increase in the volatility of most of the series studied. Second, these results should not be
viewed as a criticism of the work summarized in Burns and Mitchell (1946). These authors
were careful to point out the limitations of their reference dates. 21 Their primary interest was
not in the reference dates and the lengths of cycles, but in how individual series moved over
the cycle. No analysis has been offered in this paper to address the robustness of their finding
in this regard to changes in the prewar chronology.
-17-
Endnotes
L Also see Bailey (1978), Zarnowitz and Moore (1986), Delong and Summers (1986) and Balke
and Gordon (1986).
2. Burns and Mitchell (1946) give the official definition of contractions and expansions. These
are phases of the business cycle, which they defined as:
"Business cycles are a type of fluctuation found in the aggregate economic activity of nations
that organize their work mainly in business enterprises: a cycle consists of expansions occurring
at about the same time in many economic activities, followed by similarly general recessions,
contractions, and revivals which merge into the expansion phase of the next cycle; this sequence
of changes is recurrent but not periodic; in duration business cycles vary from more than one
year to ten or twelve years; they are not divisible into shorter cycles of similar character with
amplitude approxim ating their own."
Historically, phases for specific series and business cycle reference dates have been determined
judgementally.
3. The Bry and Boschan programs are described and applied in a novel and interesting way in
King and Plosser (1989).
4. The Burns and Mitchell dates are from Chart 53, page 373.
5. See Lehm ann (1975) for a general discussion of the statistic and Diebold and Rudebusch
(1991) for a discussion in the context of tests for business cycle phase durations. The
standardized form of the statistic shown in the table is asymptotically standard normal. (Its
exact sampling distribution can also be deduced.) In large samples, a standard t-test can also be
used to compare the average durations. For the data considered here, the results using a
standard t-test are similar to the results using the W statistics.
6. Shapiro (1988) examines prewar and postwar stock price volatility and finds no significant
difference between the periods.7
0
1
9
8
7. This postwar index is a weighted average of industrial production indices for metals,
mining, food, apparel products, and rubber and plastics. It is fully described in the data
appendix. The M iron-Romer index for the prewar period is composed prim arily of materials as
opposed to products. Materials account for approximately 40% of the postwar IP and products
account for the rem aining 60%. The materials and product components have markedly different
average phase durations in the postwar period: the materials component of industrial production
has average contraction and expansion durations of 145 months and 31.1 months, respectively;
the corresponding values for the products component is 14.7 months and 57.7 months.
8. I thank Christina Romer for supplying me with these data.
9. Steve Davis suggested this potential explanation.
10. Diagnostic tests, checking the statistical adequacy of the model, are not presented.
Undoubtedly, these tests would suggest that the model is too restrictive and is not an adequate
statistical description of the postwar data. This should not be too troubling: the purpose of the
estimated model is not to test a null hypothesis or to construct forecasts, circumstances in which
the misspecification could be very important. R ather the estimated model is to serve as a
benchm ark for some experiments that will give some rough answers to questions about the
prewar and postwar data. A careful analysis of these and related data postwar data using
dynamic factor models in contained in Denson (1992).
-18-
11. Results are shown for the broken trend models. Results for the single trend models are
very similar.
12. More detailed and thorough reviews of the procedure can be found in Moore and
Zarnowitz (1986), Diebold and Rudebusch (1991), Romer (1991b) and of course Burns and
M itchell (1946).
13. These data are from Burns and Mitchell (1946), table 17 and page 81, footnote 24.
14. Burns and Mitchell, page 74.
15. This was Babson’s index of the physical volume of business, see Burns and Mitchell, page
73.
16. See Burns and Mitchell, table 21.
17. Romer (1991b) carries out a similar exercise using the Miron-Romer IP series over the
1884-1927 period and a dating algorithm similar to the Bry-Boschan algorithm. She finds that
contractions are 3.2 months longer using the detrended data and expansions are 3.4 months
shorter.
18. My impression from reading the business press during the 1980’s and 1990’s is that a
postwar annals would greatly overstate the cyclical variability of the economy.
19. An alternative approach to determining the reference dates is to extract a single factor
from a dynamic factor model estimated using these data series. Turning points in this extracted
factor could then be determined by the Bry-Boschan program. I experimented with this
approach, but found it unsatisfactory. The results from this procedure depend critically on the
variance of the factor relative to its average drift. Unfortunately, both the variance and drift
are econometrically unidentified in a factor model and must be determined judgementally. I
chose instead to apply judgement to the turning point data directly.
20. Each of these periods corresponded to a marked slowdown in economic activity as
measured by the NBER experimental coincident index. These slowdowns were not severe
enough to be regarded as recessions.
2L See, in particular Chapter 4 of Burns and Mitchell (1946).
-19-
References
B a iley , M .N. (1978), "Stabilization P o licy and P rivate E co n o m ic Behavior,"
BPEA,
1,11-50.
Balke, N.S. and R J. Gordon (1989), "The Estimation of Prewar Gross National Product:
Methodology and New Evidence," Journal o f Political Economy, 94, 38-92.
Bry, G. and C. Boschan (1971), Cyclical Analysis o f Time Series: Selected Procedures and
Computer Programs, New York: Columbia University Press for the NBER.
Burns, A. F. (1960), "Progress Towards Economic Stability," American Economic Review, 50,119.
Burns, A. F. and W.C. M itchell (1947), Measuring Business Cycles, New York: National Bureau
of Economic Research.
Delong, J.B. and L. H. Summers (1986), "The Changing Cyclical V ariability of Economic
Activity in the United States,"in Robert J. Gordon (ed.), The American Business Cycle:
Continuity and Change. Chicago: University of Chicago Press.
Denson, E.M. (1992), "An Analysis of Postwar Employment Data," forthcoming.
Diebold, F.X. and G.D. Rudebusch (1991), "Have Postwar Economic Fluctuations Been
Stabilized?' manuscript, University of Pennsylvania.
Frickey, E. (1947), Production in the United States, 1860-1914. Cambridge, Mass: H arvard
University Press.
Kendrick, John W. (1961), Productivity Trends in the United States, Princeton: Princeton
University Press.
King, R.G. and C.I. Plosser (1989), "Real Business Cycles and the Test of the Adelmans,"
manuscript, University of Rochester.
Lehmann, E.L. (1975), Nonparametrics, San Francisco: Holden Day.
Miron, J.A. and C.D. Romer (1990), "A New Monthly Index of Industrial Production, 18841940," Journal o f Economic History, Vol. L, 321-337.
Mitchell, Wesley C. (1927), Business Cycles: The Problem and Its Setting, New York: N ational
Bureau of Economic Research.
Moore, G.H. and V. Zarnow itz (1986), "The Development and Role of the N BER’s Business
Cycle Chronologies," in Robert J. Gordon (ed.), The American Business Cycle: Continuity
and Change. Chicago: University of Chicago Press.
Romer, Christina D. (1986a) "Spurious Volatility in Historical Unem ploym ent Data," Journal o f
Political Economy, 94,1-37.
_____________________ (1986b), "Is the Stabilization of the Postwar Economy a Figm ent of the
D ata?' American Economic Review, 76, 314-334.
____________________ (1989), 'T he Prewar Business Cycle Reconsidered: New Estim ates of
Gross N ational Product, 1869-1908," Journal o f Political Economy, 97,1-37.
-20-
____________________ (1991a), 'T he Cyclical Behavior of Individual Production Series, 18891894," Quarterly Journal o f Economics, 106,1-32.
____________________ (1991b), "Remeasuring Business Cycles: A Critique of the Prewar NBER
Reference Dates," manuscript, U.C. Berkeley.
Shapiro, M.D. (1988), "The Stabilization of the U.S. Economy: Evidence from the Stock
Market," American Economic REview, 78, 1067-1079.
Shaw, William H. (1947), The Value o f Commodity Output Since 1869, New York: NBER.
Thorpe, W illard L. (1926), Business Annals, National Bureau of Economic Research, New York.
Zarnowitz, V. and G.H. Moore (1986), "Major Changes in Cyclical Behavior," in Robert J.
Gordon (ed.). The American Business Cycle: Continuity and Change. Chicago: University of
Chicago Press.
-21-
D a ta
A p p e n d ix
This appendix describes the prewar and postwar data used in the paper. All
of the postwar data, unless otherwise noted are from Citibase. All of the
prewar data, unless otherwise noted are from the NBER Business Cycle Database.
Prewar Data
Annual Data:
The sources for annual data are given in the tables and the text.
Monthly Data:
Monthly Series:_______
Pig Iron Production
Rail Road Stock Prices
NYSE Volume
Bank Clearings
Business Failures
RR Bond Yields
Commercial Paper
Building Plans
RR ton-miles
Wholesale Price Index
Total Exports
Total Imports
NBER BCD ID Number
m01585
mll032/m04008
mll006
ml2051/m04008 linked to
m09144/m04008
ml3024
ml3111
m02245/m04008 linked to
m03032 linked to m03033
m04010 linked to m04011
m07007/m04008
m07068/m04088
ml2052/m04008 in 1919
m02246/m04008 in 1899:2
in 1922:12
in 1914:12
The S&P and Dow Jones nominal stock prices are from Moore (1961). They were
deflated by the NBER BCD series m04008 (an index of the general price level).
The prewar monthly industrial series is from Miron and Romer (1990).
Transformations:
Many of the series required some preprocessing. In most cases this was to
correct obvious coding errors in the NBER Business Cycle Database. The
specific transformations were:
M01585:
10 was subtracted from the observation in 1880:11
Observations in 1926:1, 1928:1 and 1930:1 were multiplied by 10.
M11032:
Missing values in 1872:4 and 1914:8-1914:11 were estimated by linear
interpolation.
M13024:
Missing values in 1857:9-1857:10 were estimated by linear interpolation.
M02246:
Missing values in 1929:3:9-1929:4 were estimated by linear interpolation.
The series was then seasonally adjusted using the RATS exponential moving
average procedure.
-22-
Miron and Romer Industrial Production:
The series was then seasonally adjusted using the RATS exponential moving
average procedure.
M03033
The series was then seasonally adjusted using the RATS exponential moving
average procedure.
M07068:
Missing value in 1867:12 was estimated by linear interpolation.
Postwar
Annual Data:
The sources of annual data are given in the tables and the text.
Monthly Series:
Description:__________________________
Industrial Production
Industrial Production, materials
Industrial Production, products
Industrial Production, metals
Industrial Production, iron and steel
Consumer Price Index
Manufacturers Shipments
Exports
Imports
S&P Industrials
S&P Transportation
S&P Composite
Dow Jones Industrials
NYSE Volume
Corp. Bond Yield (AAA)
Ind. Bond Yield (AAA)
Corp. Bond Yield (BAA)
Ind. Bond Yield (BAA)
Commercial paper rate
Business Failures
Producer Prices
Building Permits
Total Nonag Employment
Construction Employment
Manufacturing Employment
F.I.R.E. Employment
Mining Employment
Government Employment
Service Employment
Wholesale and Retail Trade Emp.
Trans, and Pub. Util. Emp.
-23-
Citibase label
IP
IPM
IPP
IPDM2
IPDM3
PUNEW
MFGS/PUNEW
F6TED/PUNEW
F6TMD/PUNEW
FSPIN/PUNEW
FSPTR/PUNEW
FSPCOM/PUNEW
FSDJ/PUNEW
FSVOL
FYAAAC
FYAAAI
FYBAAC
FYBAAI
FYCP
FAIL
PW
HSBP
LPNAG
LPCC
LPEM
LPFR
LPMI
LPGOV
LPS
LPT
LPTU
Bank Clearings: debits (demand deposits) at other than NY banks is from the
Federal Reserve Bulletin. The nominal values were deflated by the CPI
(Citibase series PUNEW).
The Postwar Approximation to the Miron-Romer Index of Industrial Production:
The approximate Miron-Romer IP series for the postwar period is calculated as:
IPMR - wmet*ipdm2+wmin*ipmin+wfood*ipnfo2+wapp*ipnt3+wrub*ipnch5/w
where:
wmet-Ol.91+2.13
wfood-2.53+5.42+7.76+9.28+2.18
wmin=9.92+2.54+3.85
wapp«ll.89+4.62
wrub-5.97, and
w*wmet+wfood+wmin+wrub+wapp
Wmet represents the composite weight in Miron and Romer given to (i) Pig Iron
Capacity, and (ii) Tin Imports.
Wfood represents the composite weight in Miron and Romer given to (i) Sugar
Meltings at Four Ports, (ii) Cattle Receipts in Chicago, (iii) Hog Receipts
in Chicago, (iv) Minneapolis Flour Shipments, and (v) Coffee Imports.
Wmin represents the composite weight in Miron and Romer given to (i) Anthracite
Coal Shipments, (ii) Connellsville Coke Shipments, and (iii) Crude Petroleum
Products, Appalachian Region.
Wapp represents the composite weight in Miron and Romer given to (i) Wool
Receipts at Boston, and (ii) Raw Silk Imports.
Wrub represents the composite weight in Miron and Romer given to Crude Rubber
Imports.
Transformations:
LPTU:
An outlier in 1983:8 was replaced with a linearly interpolated value.
LPMI:
This series was adjusted for outliers as follows. First the trends was
removed from the logarithm of the series using a Hodrick-Prescott filter.
Second, extreme observations (greater 3 standard deviations) were set equal
to the mean. Finally, this adjusted series was then added to HodrickPrescott trend and the series was exponentiated.
F6TED:
The series was then seasonally adjusted using the RATS exponential moving
average procedure.
F6TEM:
The series was then seasonally adjusted using the RATS exponential moving
average procedure.
-24-
A v era g e
Series
T a b le 1
P h a se D u r a tio n s ,
Sample Period
X
<
7
C
M o n th ly
D a ta
E
wc
WE
A. Financial Markets
RR Stock Index
1860:1
1929:12
1.67
14.73
20.1
25.9
S&P Industrials
1947:1
1990:12
3.49
16.14
17.9
25.4
-0.5
0.1
-0.4
1970:1
1990:12
1.97
21.70
21.3
29.0
0.2
-0.3
-0.3
S&P Composite
1871:1
1929:12
1.79
15.19
17.9
24.9
S&P Composite
1947:1
1990:12
3.08
15.67
17.3
25.9
-0.4
-0.2
-0.5
Dow Jones Irid.
1897:1
1929:12
4.07
20.85
17.9
22.7
Dow Jones Ind.
1947:1
1990:12
2.20
16.04
21.2
26.7
0.4
0.7
-1.3
Bus. Failures
1894:1
1929:12
0.40
49.73
29.3
30.9
15.9
28.8
-0.9
2.4
0.1
-0.7
1.1
-1.6
-0.1
S&P Transportation
Bus. Failures
1948:1
1990:9
NYSE Volume
1875:1
1929:12
5.75
52.06
19.1
19.1
1990:12
11.79
28.91
14.7
37.4
NYSE Volume
1947:1
9.20
44.66
RR Bond Yields
1857:1
1929:12
-0.05
1.76
18.8
22.2
Corp. Bond Yields (AAA)
1947:1
1990:12
0.16
0.97
21.9
29.4
-0.8
-0.9
Ind. Bond Yields (AAA)
1947:1
1990:12
0.16
0.93
21.0
30.3
-0.8
-0.4
-0.4
Corp. Bond Yields (BAA)
1947:1
1990:12
0.17
1.09
24.7
28.1
-0.8
-1.5
-0.5
Ind. Bond Yields (BAA)
1947:1
1990:12
0.18
1.04
22.1
30.9
-0.9
-0.9
-1.0
Com. Paper Rate
1857:1
1929:12
-0.04
0.34
29.1
24.9
Com. Paper Rate
1947:1
1990:12
0.16
1.91
18.3
35.0
-0.6
1.4
-2.1
Bank Clearings
1875:1
1929:12
3.56
7.25
13.4
24.9
Bank Clearings
1970:1
1990:12
8.93
6.69
13.0
117.0
-2.4
-0.8
-1.9
B. Production Indicators
Pig Iron Prod.
1877:1
1929:12
6.08
30.78
12.9
28.0
IP Metals
1947:1
1990:12
1.00
20.16
19.2
22.7
0.9
-1.7
0.9
IP Iron and Steel
1947:1
1990:12
0.16
27.14
19.8
22.1
1.0
-1.7
1.0
2.6
-1.0
0.5
RR Freight ton-miles
1866:8
1929:12
6.60
10.48
12.3
38.9
Man. Shipments
1947:1
1990:12
2.25
6.89
14.5
33.8
23.0
IP - Miron/Romer
1884:1
1929:12
4.53
16.59
10.8
IP
1947:1
1990:12
3.65
6.26
13.1
50.3
0.3
-1.3
-2.1
IP - Miron/Romer (App)
1947:1
1990:12
1.76
10.01
13.9
22.4
0.9
-0.8
0.3
0.3
-0.4
-0.5
-0.1
-0.6
0.3
-1.1
1.3
0.1
0.7
-0.7
-0.5
C. Foreign Trade
Exports
1866:7
1929:12
4.37
19.90
13.4
27.9
Exports
1977:1
1990:12
2.89
11.38
20.3
29.3
Imports
1866:7
1929:12
3.85
21.07
17.2
33.0
Imports
1977:1
1990:12
3.30
9.48
20.0
32.0
D. Other Indicators
Wholesale Prices
1890:1
1929:12
1.36
11.65
17.4
28.2
Producer Prices
1947:1
1990:12
3.52
4.92
13.1
50.1
Plans for New Buildings
1868:1
1929:12
5.63
73.66
16.6
19.1
Building Permits
1947:1
1990:12
-0.20
29.57
18.0
19.8
Notes to Table 1: E and C respectively denote average lengths of expansions
and contractions (in months), X denotes the average annual growth rate in the
series, a denotes the standard deviation of the annual growth rate, and t^ is
the (autocorrelation robust) t-statistic for testing equality of growth rates
across the two time periods. W , and Wg are the standardized Wilcoxon rank sum
^
statistics for comparing the prewar and postwar durations of contractions and
expansions, respectively.
A v era g e
T a b le 2
P h a se D u r a tio n s ,
BEGIN-1929
E
3.1
2.8
0.4
1.2
1.21
1.14
3.1
2.8
0.6
1.7
1.18
1.29
3.1
5.4
0.9
1.1
1.28
1.18
4.7
1.2
1.2
5.7
1.3
3.4
3.4
1990
5.5
1.5
3.6
4.8
4.6
1.3
4.1
5.8
3.6
pr
tX
4.7
1990
a
po
X
1869
pr
S e r ie s
1946-END
GNP - Balke-Gordon 1869
pr
X
C
po
End
pr
C
E
po
Begin
Series
A n n u al
po
<
j
a
pr
/ <j
po
E /E
pr po
C /C
pr po
A, Aggregate Series
GNP - Romer
1869 1947-89
Comm. Output
1.1
(Shaw, NIPA)
IP
1866--1914 1947-82
4.5
1.5
4.6
8.8
1.7
1.5
0.2
8.6
3.2
1.0
2.65
1.00
2.0
1.8
0.02
.61
2.1
1.6
0.01
.45
0.1
1.4
0.95
1.13
(Frickey-Romer)
Unemp - Romer
1890 1950-80
B. Manufacturing
Canned Tomatoes
1885
1983
2.1
1.4
2.4
40.0
1.5
1.4
3.0
18.6
-0.1
2.1
1.36
1.00
Rails Shipped
1869
1985
2.2
1.6
2.0
24.0
2.1
1.5
-1.9
24.6
0.9
1.0
1.05
1.07
Canned Corn
1885
1983
2.4
1.7
4.0
34.8
1.6
1.6
1.3
18.1
0.5
1.9
1.47
1.04
Distilled Spirits
1870
1982
2.3
1.6
2.2
20.3
2.1
1.8
-1.2
16.2
0.9
1.3
1.11
0.92
0.78
Cigars
1870
1984
3.4
1.6
2.3
6.5
1.8
2.1
-0.7
10.8
1.5
0.6
1.89
Coffee imported
1870
1984
1.4
1.2
2.7
15.4
1.3
1.5
-0.6
10.9
2.1
1.4
1.09
0.79
Raw Steel
1869
1985
2.1
1.3
7.4
24.7
1.8
1.5
0.9
15.2
2.1
1.6
1.13
0.88
Cotton consumed
1870
1984
2.3
1.3
3.2
10.8
1.5
2.1
-1.3
9.8
3.0
1.1
1.59
0.60
Cigarettes
1870
1984
10.0
2.3
9.5
12.5
3.0
1.1
2.0
3.2
1.4
3.9
3.33
2.07
Tob. and Snuff
1870
1985
2.9
1.2
2.5
7.4
1.9
3.0
-1.9
5.0
5.1
1.5
1.56
0.39
Beer
1870
1984
3.6
1.5
-1.2
19.9
4.1
1.3
2.1
2.7
-0.7
7.3
0.87
1.17
C. Agriculture
Corn Production
1866
1985
1.9
1.2
1.8
19.1
1.9
1.3
1.0
18.2
0.3
1.0
1.02
0.97
Wheat
1866
1985
1.8
1.5
1.7
17.4
1.8
1.9
1.7
14.0
0.0
1.2
0.99
0.78
Irish Potatoes
1866
1985
1.3
1.5
1.9
21.8
1.5
1.2
0.4
10.2
0.9
2.1
0.83
1.27
Rye
1866
1985
1.7
1.7
1.0
17.0
2.2
1.7
0.8
25.4
0.0
0.7
0.76
0.99
Cotton
1866
1985
1.8
1.0
2.9
19.2
1.7
1.6
-0.3
24.1
0.9
0.8
1.03
0.64
Barley
1866
1985
2.0
1.1
4.0
17.1
1.8
1.5
2.1
14.9
0.8
1.1
1.06
0.75
Hay
1866
1985
2.9
1.4
2.1
10.5
2.0
1.0
0.9
5.5
1.0
1.9
1.46
1.36
Flaxseed
1866
1984
1.7
1.9
1.1
34.0
1.2
1.6
-4.2
35.6
1.0
1.0
1.35
1.22
0.79
Tobacco (Raw)
1866
1985
1.7
1.3
2.6
21.6
1.9
1.6
-0.4
15.6
1.4
1.4
0.89
Sweet Potatoes
1868
1984
1.5
1.4
1.8
15.4
1.3
1.4
-1.9
15.4
1.7
1.0
1.17
1.01
Oats
1866
1984
2.3
1.4
2.6
17.1
1.4
2.0
-2.6
14.8
2.6
1.2
1.68
0.68
D. Minerals
Gold
1869
1984
2.2
2.1
-0.3
10.0
1.5
2.8
0.0
13.4
-0.1
0.7
1.49
0.78
Phosphate Rock
1880
1984
2.4
1.3
3.7
20.6
9.3
1.8
4.1
7.9
-0.1
2.6
0.25
0.76
Iron Ore
1889
1984
2.7
1.2
3.9
25.7
1.8
1.3
-1.9
22.7
1.5
1.1
1.54
0.96
Lead
1886
1984
2.8
1.1
4.2
9.7
1.5
1.5
1.4
14.4
1.2
0.7
1.92
0.73
0.83
Silver
1869
1984
2.4
1.5
1.4
9.8
1.8
1.8
1.6
13.0
-0.1
0.8
1.35
Pig Iron
1869
1983
2.7
1.4
5.5
22.2
1.8
1.3
-0.1
16.3
2.0
1.4
1.52
1.09
Copper
1869
1984
4.2
1.1
7.3
20.4
1.8
1.5
1.7
15.8
1.8
1.3
2.30
0.71
Bituminous Coal
1869
1984
3.6
1.1
5.1
10.8
2.3
1.4
1.0
9.8
2.3
1.1
1.56
0.77
Pyrites
1882
1982
2.2
1.5
2.9
20.7
1.7
2.4
1.0
18.7
0.4
1.1
1.32
0.61
Cement
1880
1984
6.3
1.2
8.3
11.2
3.4
1.5
0.2
6.9
3.4
1.6
1.86
0.78
Coke
1880
1984
2.7
1.2
5.2
22.2
1.8
1.5
-2.2
13.8
2.5
1.6
1.47
0.75
Zinc
1869
1984
5.3
1.4
7.1
17.9
2.1
2.4
-1.5
10.2
3.0
1.8
2.47
0.57
Crude Petroleum
1869
1984
5.3
1.2
8.9
13.1
3.7
1.6
1.3
4.4
4.1
2.9
1.45
0.78
Anthracite Coal
1869
1984
1.7
1.4
2.6
17.8
1.0
5.3
-6.8
9.9
4.9
1.8
1.67
0.27
T a b le
2 ,
c o n tin u e d
E.
Summary of results, by whether the postwar
and prewar growth rates differ statistically
txgl < 1
^Xgl < 2
> i
Epr' po :
/E
10
5
18
6
C /C
:
pr' po
Notes:
< 1
5
10
9
15
Panel A-D:
> 1
< 1
E and C respectively denote average lengths of expansions
and contractions (in years), X denotes the average growth rate in the series,
o denotes the standard deviation of the growth rate, and t^
is the
(autocorrelation robust) t-statistic for testing equality of growth rates
across the two time periods. The ratios <pr' po , EL /E__* and pr' po are
r Vcr
/(L
r
pr' po
(respectively) the ratios of standard deviations, average expansion lengths and
contraction lengths prewar relative to postwar.
Panel E:
The entries are the number of series in Panel B-D (the
disaggregated series) that fall in the indicated category.
For example, among
the 15 series with |t^|<l, 10 have prewar expansions at least as long as
postwar (Epr/Ep0>l) and 5 have postwar expansions longer than prewar
<
W
1’ •
T a b le
3
Average Phases, Shares and Growth Rates for Nonagricultural Employment
1947- 1990
Series
C
- Share of Total Emp.
E
1869
1929
K
1948
Avg. Growth Rate (X)
1948
1990
1869-1937
1947-1990
Total
11.7
54.8
100.0
100.0
100.0
100.0
100.0
2.7
2.1
Manufacturing
17.4
31.8
34.4
28.4
30.7
34.7
17.3
2.4
0.4
—
—
21.7
17.8
15.1
11.5
25.6
2.3
4.0
Trade
10.8
88.8
15.2
21.5
22.7
20.7
23.7
3.2
2.7
Tran+Com+PU
15.0
30.9
9.9
11.0
8.5
9.3
5.3
2.4
0.8
Construction
15.8
45.9
9.5
6.4
6.6
4.9
4.7
1.7
2.2
Government
17.0
192.5
6.0
7.8
10.5
12.6
16.6
3.8
2.7
Mining
26.6
30.8
2.5
2.8
2.0
2.2
0.7
2.7
-0.5
0.8
4.3
3.8
4.0
6.2
5.0
3.2
Services
F.I.R.E.
--
—
Notes: E and C respectively denote average lengths of expansions and
contractions (in months).
The shares for 1869, 1929 and 1948
Kendrick (1961, p. 308), Table A-VII.
establishment survey.
are from
Data for 1947-1990 are from the BLS
Table 4
Average Phases for Data Generated by the Trend-VAR Model
A.
Single Prewar and Postwar Trends
Generated Data Prewar Trends
Generated Data Postwar Trends
Aggregate Postwar Employment
NBER Prewar
NBER Postwar
B.
C
11.9 (.99)
13.1 (.14)
11.7
21.2
10.7
E
74.1 (.00)
57.6 (.38)
54.8
26.5
49.9
Trend Breaks Every 20 Years
Generated Data Prewar Trends
Generated Data Postwar Trends
Aggregate Postwar Employment
NBER Prewar
NBER Postwar
C
12.7 (.99)
13.5 (.10)
11.7
21.2
10.7
E
70.0
55.7
54.8
26.5
49.9
Notes: E and C respectively denote average lengths of expansions and
contractions (in months). The numbers in parentheses are the percentiles of
the distribution of average phase durations for the generated prewar and
postwar data corresponding the NBER average phase durations.
Table 5
Dynamic Factor Model
A. Estimated Model
aiFt + u£
^iut i + €t- var<£t> - 4
+ ^2Ft-2 + et ’ Var<et^ “ 1 -°
Sector
Manuf ac tur ing
Services
Trade
Tran+Com+PU
Construction
Government
Mining
F.I.R.E.
a
.0030
.0006
.0012
.0016
.0029
.0002
.0027
.0004
o
.0044
.0018
.0018
.0051
.0120
.0036
.0179
.0017
_______ B
L
.97
.99
.99
.94
.96
1.00
.99
1.00
< ^ = 1.788, <> * - .797
i
>
j2
B. Average Phases for Data Generated by the Trend-Dynamic Factor Model
E
C
1. Parameters from Estimated Model
Generated Data Prewar Trends
Generated Data Postwar Trends
11.6 (.99)
12.2 (.28)
2. Prewar Results Using Modified Parameters
13.2 (.99)
Multiplied by /2, of Reduced
13.1 (.99)
a ^ Multiplied by Jl
16.3 (.97)
a^ Multiplied by 3
18.2 (.82)
Multiplied by 5
a
82.1 (.00)
65.7 (.25)
52.7
52.5
31.1
26.6
(.00)
(.00)
(.08)
(.40)
Notes: In panel A, x1^ denotes the deviations of the logarithms of the data
.
from trend. The trend is of the form Aq + A-^t + A2t(I(t>r)), where I(.) is
the indicator function and r is 1967:12. The model was estimated using data
from 1947:1-1990:12. The restriction Var(et)~l is a normalization that serves
to identify the a^. In Panel B the numbers in parentheses are the percentiles
of the empirical distribution corresponding to the NBER average phase
durations.
In the first row of Panel B.2, of is reduced by af , so that
the variance of xt is unchanged.
T a b le
6
A. Peak and Trough Dates, Selected Series
Approximate IP
Miron-Romer
Ind. Prod.
(Less Steel)
P
Building
Iron & Steel
*
T
E
T
Stock Prices
Permits
Com. Paper
P
T
48.6
49.10
48. 10
49.10
47.10
49.1
51.1
51.7
51. 6
52.7
50.7
53.12
53. 7
54.4
52.11
53.9
57.3
58.4
55. 9
58.4
55.2
58.2
60.4
61.1
59. 6
62.6
58.11
60.12
Exports
T
P
Imports
T
P
Reference
T
P
51.7
53.7
P
T
P
Pseudo-
Rate
(S&P Comp.)
NBER
T
P
T
48.11
49.10
49.6
49.3
50.2
48.8
49.10
51.4
52.1
53.1
53.9
53.6
54.12
53.7
54.2
53.7
54.5
56.7
57.12
57.10
58.7
56.6
58.4
57.8
58.4
60.1
61.7
59.11
61.9
60.4
61.2
64.2
67.6
66.1
66.11
67.6
60.10
62.10
65.4
66. 10
59.7
61.12
66.1
66.10
66.10
67.6
66.10
69.11
70.7
69. 11
71.8
69.2
70.1
68.12
70.7
69.12
72.2
69.11
71.1
69.12
70.11
73.9
75.3
73. 12
75.7
72.12
75.3
73.1
74.12
74.7
76.12
73.10
75.5
73.11
75.3
78.7
79.7
78. 11
80.7
77.8
80.4
76.7
80.4
78.9
80.1
80.1
80.7
81.7
82.12
81. 2
82.12
80.9
81.10
80.11
82.8
81.5
83.1
80.4
83.5
77.11
80.2
83.3
81.4
82.12
81.7
82.11
84.6
85.1
84. 2
87.1
84.2
84.10
83.7
84.7
84.7
86.9
85.1
86.7
84.7
85.3
84.4
86.1
86.1
86.9
86.4
88.1
89.10
90.4
89.3
90.6
89.6
87.8
89.11
89.1
88.10
89.12
89.3
90.7
90.8
B. Specific Cycle Lead and Lags Relative to Reference Cycle
Approximatei IP
Ind. Prod.
Building
Stock Prices
Com. Paper
(Less Steel)
Miron-Romer
Iron & Steel
Permits
(S&P Comp.)
Rate
P
T
P
T
P
49.10
-2
0
2
0
-10
51.4
52.1
-3
-6
2
6
-9
-6
53.7
54.2
C
)
-2
0
2
-8
-5
56.6
58.4
S
>
0
-9
0
-16
-2
59.11
61.9
!
>
-8
-5
9
12
-9
0
-9
-7
Pseudo
Reference
P
48.8
T
0
T
P
Exports
T
P
T
-4
7
-6
-5
-1
10
1
-4
-4
-11
2
-2
-9
-8
0
Imports
4
0
-9
66.10
67.6
69.11
71.1
-6
0
7
-9
-12
-11
-6
1
75.5
-1
-2
2
2
■10
-2
-9
-5
9
T
P
T
13
73.10
P
19
)
C
78.9
80.1
-5>
-6
2
6
■13
3
-26
3
81.4
82.12
2
)
0
-2
0
-7
-14
-5
-4
1
1
-12
5
-14
3
84.4
86.1
2
>
c
-12
-2
12
-2
-15
-9
-18
3
8
9
6
3
-10
1.2
-3.2
89.6
AVG
-5
-1.2
-8
3.4
8.7
-6
-7.7
4
-3
-22
-4.8
1.5
4.1
0.3
-3
2.8
-3.5
-1.8
Table 6, continued
C. Average Phase Lengths from NBER and Pseudo Reference Dates
1.
2.
3.
Prewar (NBER)
Postwar (NBER)
Postwar (Pseudo Ref.)
c
20.5
10.7
15.6
E
25.3
49.9
28.9
WC
WE
2.83
0.67
-2.62
-0.17
Notes: Panel A: P and T refer to peak and trough dates. For the individual
series these were determined by the Bry-Boschan algorithm. The column labeled
Pseudo-Reference is a set of reference dates chosen from the peak and trough
dates of the individual series. The column labeled NBER contains the NBER
peak and trough dates. Panel B: This table shows the difference between the
peak and trough dates of the specific series and the pseudo-reference peak and
trough dates. Panel C: E and C respectively denote average lengths of
expansions and contractions (in months).
and Wg are the standardized
Wilcoxon rank sum statistics for comparing the prewar and postwar contractions
and durations, respectively.
F ig u r e
1
Pigiron Production
Bry-Boschan and Burns and Mitchell Peaks and Troughs
1877
1882
1887
1892
1897
1902
Note:
1907
1912
1917
1922
1927
Solid Lines are Bry-Boschan Peak and Trough Dates
Arrows are Burns and Mitchell Peak and Trough Dates
@FedFRASER