To examine why industries with steeply rising post-trough prices are more common than
industries with steeply falling prices, we look at the identity of the industries exhibiting large
price changes. Table 6 lists four-digit industries for which the deflated post-trough price rose or
fell by more than 10% over the next one or two years.

[INSERT TABLE 6 HERE]
Industries were selected by comparing price in November 1982, the NEBR trough, with price
in November 1983 and November 1984. The list of industries suggests that the bias in favor of
higher prices is not a statistical artifact of common causes affecting pricing in similar industries;
the industries experiencing price increases are perhaps more diverse than those experiencing
declines.

We next examine whether the industries listed in Table 6 that increased price did so in
response to increases in the price of inputs. We find that industries experiencing large price
increases did not on average experience greater increases in either wage rates or the cost of
raw materials than industries that did not raise price. To determine whether higher wages are
the source of the observed price increases, we examine industries in which the labor cost share

of total variable cost exceeded 20%, according to the 1982 Census of Manufactures. For

those industries, we find that neither average hourly earnings nor overtime levels increased more
than the average increase for all manufacturing industries.

Similarly, we examine input prices for those industries in which materials inputs (at the
five- to six-digit SIC code level) account for more than 10% of total delivered materials costs,

according to the 1982 Census Industry Surveys. On average 2.1 such inputs were identified

16


yQ6l taqueaceq 0} 786, Asonuor

November 1982=1C0
96 100 104 108 1

12

wT
VX

| aunbry

S]ONPOId Ply 2 S|OsWaYD :BZ OIS ‘seoug WHIG—y


REFERENCES

11.

12.

Baker, J., 1989, "Identifying Cartel Policing Under Uncertainty: The U.S. Steel
Industry, 1933-1939," Journal of Law and Economics, 32, $47-S76.

Baker, J. and Bresnahan, T., 1992, "Empirical Methods of Identifying and Measuring
Market Power,” Antitrust Law Journal, 61, 3-16.

Baker, J., Johnston, T., and Woodward, P., 1990, "Asymmetric Oligopoly Behavior in
the Aluminum Industry Around a Business Cycle Trough," Amos Tuck Schoo! Working
Paper #256.

Barsky, R. & Warner, E., 1993, "The Timing and Magnitude of Retail Sales
Markdowns With Respect to Weekends and Holidays," unpublished manuscript.

Bills, 1987, "The Cyclical Behavior of Marginal Cost and Price," American Economic
Review, 77, 838-855.

Blinder, A., “Why are Prices Sticky? Preliminary Results From an Interview Study,”
American Economic Review Papers & Proceedings, May 1991, 89-96.

Bresnahan, T., 1982, “The Oligopoly Solution Concept is Identified," Economics
Letters, 10, p. 87-92.

Bresnahan, T. and Suslow, V., 1989a, "Short Run Supply with Capacity Constraints,"
Journal of Law and Economics, 32, p. 511-542.

Bresnahan, T. and Suslow, V., 1989b, "Oligopoly Pricing with Capacity Constraints,"
Annales d'Economie et Statistique, 15/16, pp. 267-289.

Carlton, D., 1989, "The Theory and Facts of How Markets Clear: Is Industrial
Organization Valuable for Understanding Macroeconomics?" in Handbook of Industrial
Organization, R. Schmalensee and R.D. Willig (eds.), pp. 909-946.

Clarke, R., 1986, "SICs as Delineators of Economic Markets," Dept. of Justice
Working Paper EAG 86-18.

Domowitz, I., Hubbard, R., and Petersen, B., 1986a, “Business Cycles and the
Relationship Between Concentration and Price-Cost Margins," Rand Journal of
Economics, 17, p. 1-17.


Table 4: Correlations of Detrended 2-Digit Prices with Detrended Output in the Postwar Period Around NBER Peaks

Correlation of Qt with Pt+i

Industry i=-5 1s-4 ie-3 da-2 i=0 deed m4 da43 mtd iaeS
20 -0.16 *-0.18 -0.16 -0.15 -0.09 -0.16 -0.2 -0.13 -0.02 0.05 o.11*
ar -0.24 -0.26 -0.23 -0.19 -0.17 -O.21 -0.01 0.13* 0.1 -0.26 -0.23
22 0.2 0.21 0.2 0.22* 0.21 0.16 0.18 0.18 0.14 0.14 0.13
23 0.05 0.08 0.05 0.14 0.18* 0.08 0.09 0.13 0.09 0.16 0.17
24 0.19* 0.03 -0.03 0.01 0.06 0.05 -0.03 -0.03 0.07 0.14 0
25 -0.17 -0.17 -0.12 0.01 0.02 -0.04 -0.08 -0.07 -0.07 -0.03 0.04*
26 -0.18 -0.1 ~0.07 0.02 0.12 0.08 0.06 -0.03 -0.06 0.08 0.18*
28 -0.08 -0.07 -0.08 -0.07 -0.01 o* 0 -0.05 -O.1 -0.09 ~0.06
29 -0.03 -0.06 0.05 0.21 0.27* 0.16 -0.06 -0.24 -0.24 ~0.18 -0.13
30 -0.08 -0.08 -0.11 -0.09 -0.02 0.05 0.06 0.05 0.06 0.08 o.11*
31 0.23 0.26* 0.21 0.2 0.17 0.05 0.03 O 0 0.05 0.06
32 -0.15 -0.15 -0.13 0.02 0.13 0.1 0.18 0.19* -0.06 -0.1 -0.12
33 0.03 0.02 -0.02 0.02 0.05 i) 0.01 -0.01 -0.01 0.06 0.16*
34 0.04 0.05 0 0.01 0.01 -0.01 -0.01 -0.06 -0.06 0 0.08*
35 0 -0.02 -0.03 -0.04 -0.04 0.01 0.09 0.1 0.07 0.07 0.11*
36 -0.09 -0.12 -0.08 0.02 0.06* 0.01 0 -0.04 -0.03 0.02 0.02
37 0.15 0.42* 0.19 0.28 0.09 -0.15 -0.17 -0.03 -0.18 -0.26 -0.32

Output series is aggregate Industrial Production Index.


function (2).
(5) P, = @(P,-MR,) + c(Q,,w,,Q*) + u
= OC£,Q) + c(Q,W.Q) + U,

In equation (5), @, indexes the oligopoly solution concept. If © is equal to 1, the firms
are behaving like a monopolist (equating marginal revenue with marginal cost). If © is equal to
0, the firms are behaving competitively (equating price with marginal cost). Other forms of
oligopoly behavior will lead to values of the parameter between 0 and 1. The expression -f,Q,
is related to the elasticity of industry demand.'! Equation (5) implies that an industry’s price
fluctuates with variation in marginal cost, the elasticity of market demand,’ and oligopoly
behavior. Accordingly, for prices to change systematically over the business cycle, one of these

three components must vary cyclically.

A. Explanations for Procyclical Price Behavior

An industry’s prices might behave procyclically, rising at business cycle peaks or

declining at troughs, for several reasons. First, in many industries, increases in marginal cost will

lead price to rise in the neighborhood of a business cycle peak, when production nears full

"'The industry demand clasticity e is defined such that Le = -£,Q/P,.

So long as © > 0 in equation (5), prices will rise as demand grows less elastic and fall as
demand grows more elastic, holding constant the oligopoly solution concept and marginal cost.
Proof: For two time periods 0 and 1, assume that MC) = MC, = MC. Equation (5) then
implies P,(1 - ©,/e;) = MC = Po(1 - @p/eo). It is evident that if ©) = ©, (for © > 0), then P, >
P, if and only if e; < eg (that is, price rises if and only if demand grows less elastic).

10


34000

36000
37100
37000
38000
39000

Fabricated Metal Products
Nonelec. Mach.

Elec. Machinery

Motor Vehicles.and Parts
Transportation Equip.
Instruments

Misc. Manufactures

3411-3498
3511-3592
3612-3694
3711-3715

3732
3822-3873
3911-3999




INDUSTRY

20
2
22
23
4
ri)
26
27
28
28
Ki)
Hv
32
xB
uM
B
38
7
3B
9

TABLE 10

THE RELATIONSHIP BETWEEN PRICE CHANGE AND CONCENTRATION, BY INDUSTRY

Food and Kindred Products
Tobacco Manufactures

Textile Mill Products

Apparel and Other Textile Products
Lumber and Wood Products
Furniture and Fixtures

Paper and Allied Products

Printing and Publishing

Chemicals and Allied Products
Petroleum Refining

Rubber and Plastic Products

Lent

and Leather Products
Stone, Clay and Giass Products
Primary Metals

Fabricated MetatProducts
Nonelectrical Machinery
Electrical Machinery
Transportation Equipment
Instruments

Misc. Manufactures

% Change

0.8
08
AD
16
1B
16
BE
55

9.2
04
16
07
ral
13

13
ral
48
at

Mean Concentration

Ratio

48
58
5
28
30
7
47
3
55
36
4
5
2
5t
38
a
58
“
87
49

Concentration values are taken from the 1982 Census of Manufactures.

Number of

A Digit Industries

67
1
32
4
a
10
16
5
B
6
u
7
18
45
42
78
6
7
8
a

Corretation

Coefficient

068

028
065
052
-.008
599
345,
223
248
584
408
188
057
404
ABS
067
854
M5
-.078


and assuming that the matching month is chosen to equate quantity with the reference month
(Qy = Q; = Q) and that marginal cost does not change over the trough (MC, = MC, = MC),
the change in price has the following interpretation:
(8) P; - Po = ((MC+u,)/(1+©,B,)] - [(MC+t9)/(1+O985)]
Equation (8) shows that the systematic increases in price (not arising from variation in the
random variables u,) will reflect either more inelastic demand ({8| rises) or more cooperative
behavior (@ rises).”°

This interpretation is not sensitive to the functional form of demand. With quantity
matching and constant marginal cost, and the more general demand function (2), the quasi-
supply function (5) implies:
(9) Py - Po = QU-f,'@, + £,°Oo)
In equation (9), f,' and f,° represent, respectively, the slopes of the post and pre-trough
demand curves. In this framework, two interpretations of a price rise are worth noting. If price
increases from the reference month to the matching month (P, > Po), then cooperation is greater
post-trough (©, > @,) as long as the demand curve does not grow steeper after the trough (as
long as 1,01 2 tf,'D. In the alternative, if price increases from the reference month to the
matching month (P, > Po), then demand grows steeper (lf! > 1,01) as long as cooperation does
not decline after the trough (as long as @p 2 ©, > 0). Thus, if marginal cost is constant, either

demand grows steeper while market power is being exercised (© > 0), or the degree of

25This interpretation assumes that 1+@8 > 0. That is, if demand is highly inelastic, then the
oligopoly solution concept is very competitive.

20


1982=100

ember

Nov

Figure 2 Price, All Industries
De)
io} a a TT
ix)
ok
ob
2| Z\
7 2
o
fon
PX /
a
an
/ w= Not Seas. Ady
Koo —— — Seas. Adj
Uy beste FS CO SS SOT GE ON ST CS OS SD eT OE Oe
J o'A 0 J A J ) J A J )

January 1982 to December 1984



Market Power Explanations and the Cross-Industry Behavior

of Prices Around a Business Cycle Trough

Jonathan B. Baker and Peter A. Woodward!

1. Introduction

A large economic literature seeks to identify market power from the response of prices
and output to demand and cost shocks. Many researchers, both microeconomists and
macroeconomists, have focused particularly on the response of prices to business cycle
fluctuations.” This research problem is often framed in terms of analyzing whether prices
respond to cyclical variation in cost and demand, and, if not, whether the exercise of market
power accounts for observed price rigidities.

Much of this literature explores the behavior of prices and price-cost margins over the

‘Federal Trade Commission, and Antitrust Division, U.S. Department of Justice,
respectively. The views expressed are not necessarily those of the Federal Trade Commission
or any individual Commissioner, or those of the Justice Department. The authors are gratcful to
Matt Shapiro; seminar participants at the Department of Justice, Johns Hopkins, and
Northwestern; and the Tuck Associates Program of Dartmouth’s Amos Tuck School. Bruce
Allen, Mike Duffy, Cathy Reinig, and Jo Steele provided valuable research assistance.

Other researchers identify markct power from the response of prices to industry-specific
cost and demand shocks (see generally Baker & Bresnahan (1992)), or from the cyclical
variation of variables other than price (Hall (1988)).

1


classifies one hundred eighteen of those industries as not competitive, describing five as
controlled by a dominant firm and the remaining one hundred thirteen as "tight" oligopolies. We
compared this list of industries with the four digit industries included in Table 7 as likely to have
been exercising market power in 1980 based upon the relative behavior of price and quantity
during the 1982 recession, and reject at a 5% level the hypothesis that the two methods of
selecting industries are independent; we found more overlap than would be predicted by
chance.*? This conclusion increases the plausibility of the view that both methods in fact identify
industries exercising market power.

In contrast, Lebow’s (1991) ranking of industries according to market power is

unrelated to the rank ordering derived from our price increase series.“ The absence of a

>°Of the sixty-four four-digit industries Shepherd identifies as non-competitive, eight of those
industries appear in our list of eleven industries exhibiting market power. We identify many less
industries as likely settings for the exercise of market power than does Shepherd, for two
reasons. First, we in effect employ a tougher standard for identifying poor industry
performance than he does. Second, our method may be more difficult to satisfy than his
because we identify market power through finding cases in which market power increased as a
boom began; Shepherd also seeks to identify market power in industries in which the degree of
its exercise does not change over the business cycle. On the other hand, virtually all the
industries Shephard identifies as not competitive are classified by him as "tight" oligopolies.
Because such industries would be the most likely to vary markups cyclically in response to
variation in the elasticity of market
demand, one might have expected to see significant overlap between the two lists of industries
exercising market power.)

“Lebow created two price-cost margin series for four hundred forty-six four-digit industries
over the years 1958 to 1984. One series was derived using the procyclicality of total factor
productivity, following Hall (1988). The second margin series was constructed from Lebow’s
econometric estimates of marginal cost (through the simultaneous estimation of a translog cost
function and an associated factor demand equation for labor and materials). We are grateful to
David Lebow for sharing these series with us.

31


for each industry (ignoring catch-all input aggregates), Producer price indexes were available
for 28% of those inputs. In only one case can we fairly attribute the vast majority of a price
increase to higher input prices: the price of leather tanning rose 7% in one year following the
trough largely because the price of cattle hides rose 38% during that year. In a second
industry, wood pallets, a significant portion of the 4% (one year) and 13% (two years) price
increase can be attributed to an 18% increase (over both one and two years) in the price of
hardwood lumber. In the remaining thirteen instances in which input prices were available,
however, the observed input prices rose far less than the corresponding output prices or else
the input prices declined.*! We conclude that increases in input prices are unlikely to explain
the bulk of the observable price rises.

We cannot immediately reject another cost-related explanation for the price increases
identified in Table 6. If output grew rapidly after the trough, or if industry demand was growing
rapidly and was largely not influenced by the business cycle, prices may have risen because the
individual industry approached a capacity constraint even though the rest of the economy had
barely left the recessionary trough. But it is implausible that this explanation fits more than a
handful of the industries in the table. Indeed, it is difficult to identify any industry in the list likely

to have been facing explosive demand growth sufficient to cause industry capital to be fully

**Even when a specific material inpui with a rapidly rising price accounts for most of total
materials cost, as was true for leather tanning, the input accounts for a lower share of firm
variable costs (because the latter cost also includes labor and energy). In order for the input
price increase to explain most of the output price change, therefore, the input price must
increase by substantially more than the output price.

17


similar cyclical pattern to the more broadly defined measure in which it is nested.

Table 7 reports four- and five-digit SIC industries for which the (deflated) price in the
matching month exceeds by more than 5% the price in the reference month.”

[INSERT TABLE 7 HERE]

In some industries, sales never rise in the post-trough period to equal the quantity sold in the
reference month; these industries have been excluded from the sample. As with the previous
list of industries experiencing price increases around the trough, we found little evidence that
wage increases or increases in the price of inputs account for any significant part of the
observed industry price increase.

The industries listed in Table 7 most likely exercised market power as the boom began.
While we cannot be certain that these price changes do not reflect cost increases, it is likely that
the listed industries contain a far greater concentration of industries exercising market power
than would a random sample of industries for which price indexes are available. And, if our
conjecture about demand elasticities is correct, they are on average industries in which the

degree of cooperation increased as the boom began.

B. Consistent Price Patterns Across Recessions

Some industries consistently exhibit price increases in the early stages of a boom. This

*}Because we impose quantity matching, the price rises we observe are typically smaller
than those reported in Table 6.

23


following the 1982 recession. These industries are selected through a criterion that identifies,
on average, industries exercising market power. For each industry in our sample, we identify a
reference month six months prior to the trough and a matching month from the two years
following the trough. The matching month is chosen such that industry sales are close to their
level during the reference month. If, as we believe, firms generally experience constant returns
to scale in the neighborhood of a trough and input prices generally do not fluctuate enough to
affect prices significantly in the neighborhood of a trough,” then an increase in price between
the reference month and the matching month reflects the exercise of market power. With cost-
based explanations excluded as generally implausible, a price increase has two explanations: it
may reflect increased cooperation as the boom begins, or it may reflect more steep industry
demand given some level of industry cooperation. In either case, the industry exercises market
power.

This interpretation may be clarified by assuming that the inverse demand function (2)
has a constant elasticity form:
(6) P=aQ®Y'e”
The quasi-supply function (5) then reduces to
(7) P, = (MC,4u,)/(1+0,8,)

Denoting the before- and after-trough time periods with the subscripts 0 and 1, respectively,

The method of identifying the matching month ensures that output in the matching month
does not dramatically exceed output around the trough, thereby also reducing the possibility
that cost increases associated with a rapid increase in capacity utilization explain price increases
in our data.

19


relationship could be explained if there are many industries with both high margins and margins
that do not vary over the business cycle. Such industries would be ranked highly by Lebow,
but would not be ranked highly by our approach to the extent our method detects changes in

the level of market power.

Iv. Concluding Comment

This paper emphasizes a market power explanation for the procyclical behavior of
particular industry prices around business cycle troughs. Either firms increase cooperation as
booms begin, or demand functions grow steeper as booms begin while firms exercise some
market power over the whole period around the trough. The focus on pricing around business
cycle troughs exploits the idea that when an industry’s price rises around the trough of a
recession, the price increase is unlikely to be explained by rising input prices or binding capacity
constraints. If so, the pricing experience of industries around the troughs of recessions may be

an indicator of the exercise of market power.*!

“Tt is worth noting that not every exercise of market power calls for antitrust intervention.
For example, if fixed costs are high and marginal costs low, we might expect.to observe © > 0
ina free-entry equilibrium during at least some stages of the business cycle. Here, an above-
cost price would reflect quasi-rents necessary to cover fixed costs. Such industries exercise
what an economist would call market power, without the market power necessarily constituting
an antitrust problem. Nevertheless, the set of industries increasing prices around a business
cycle trough might be a useful starting point for antitrust enforcers seeking to target a group of
industries for more intensive investigation.

32


Third, one important oligopoly supergame model predicts that cooperative pricing is
least likely during booms. High demand periods may increase firm gains to defecting from a

cartel, leading to counter-cyclical pricing (Rotemberg & Saloner, 1986).'®

C. Propagation of Procyclical or Countercyclical Price Variation

Another source of cyclical variation in an industry's marginal cost, which can generate
cyclical behavior of price, is the transmission of price changes from one industry to other
industries selling substitutes or complements in demand, including industries that use the first
industry's product as an input.’’ In our data, price increases are most likely to propagate when
one industry buys the output of another. If the price of gold, for example, rises as a boom
commences, the price of gold jewelry will tend to rise also, especially if the input accounts for a
significant share of the variable costs of downstream production.”” Through this mechanism,

both procyclical and countercyclical price variation can spread.

cyclical component.

'84 similar cyclical pattern in oligopoly pricing could arise if firm seek to reduce the volatility
of reported earnings, perhaps because of financial market imperfections such as bankruptcy
costs or credit rationing. Such firms may raise prices in troughs and lower them in booms.
(Klemperer, 1992 §4.6)

‘Upstream products and downstream products are typically complements in demand.
2°We cannot examine this process systematically for the industries we study because we
lack an appropriate input-output table. For efforts along these lines, see Shea (1993) and Fair

(1993).

14


This conclusion is also supported by the summary statistics, also reported in Table 1, from the
following OLS regression:

(1) PUP, = By +B,(P//P,) + &,

In equation (1), P, refers to a four-digit industry, P,” refers to the corresponding two-digit
industry aggregate, and P, is the economy-wide average PPI. This equation typically gives an

R? value of about 0.42, indicating that there is substantial inter-industry variation in pricing.”

B. The Cyclical Behavior of Disaggregated Prices

To examine the cyclical behavior of prices in our disaggregated data, we adapt a
methodology employed by Kydland and Prescott (1990) with aggregate prices. KydJand and
Prescott correlated the time series of detrended aggregate output with a detrended
contemporaneous price index, and with the same price index shifted to lag or lead the quantity
index. Using aggregate data over the postwar period, they found that lagged prices have the
highest correlation with output fluctuations. They concluded that aggregate prices behave
countercyclically. Applying a similar methodology to our disaggregated data, we find
substantial cross-industry differences in the cyclical behavior of prices, and substantial within-
industry differences across different stages of the cycle (peaks vs. troughs).

To identify cross-industry variation in the cyclical behavior of prices, we corzelate the

°Similarly, Clarke (1986) finds that firms within a four-digit SIC code are no more alike than
randomly-selected industries in sales fluctuations, profit rates, or stock price fluctuations.

6


the sample is stratified into two-digit industries. Table 10 reports reference to matching month
price change and the four-firm concentration ratio for four- and five-digit industries, both

averaged within each two-digit industry.

{INSERT TABLE 10 HERE]
The last column shows that the correlation is positive between these two variables in 13 of 19
two-digit industries. If industries having ten or fewer four- and five-digit industries are
excluded, the correlation is positive in 10 of 13 industries. Although we cannot test the
statistical significance of these associations because the observations within industries are not

independent, the positive relationship between price change and concentration appears strong.

2. Import Competition

The industries identified in Table 7 as likely on average to exhibit the exercise of market
power based on price increases over recessionary troughs have a second structural
characteristic: on average they are free from import competition. We reached this conclusion
by analyzing the import market shares, reported in Table 11, for the eighteen industries for
which we were able to determine import penetration cut of the sixty industries in Table 7.

[INSERT TABLE 11 HERE]
Most of these industries had very small import shares; in twelve of eighteen, import penetration

was less than 5% of the market. Only three had impozt shares of more than the overall

29


following the trough, firms experience constant returns to scale, input prices do not increase,
and neither the degree of industry cooperation nor the slope of industry demand changes.

These assumptions are consistent with the idea that in the early months of a boom, the dorninant
influence on price and output in most industries is most likely an exogenous economy-wide
increase in demand. Under these assumptions, the price elasticity of output can be interpreted
as the elasticity of quasi-supply, and it equals the ratio of the demand elasticity to the oligopoly
solution concept parameter.”

Accordingly, we interpret a high price elasticity of output during the first six months of a
boom as suggesting either that demand is elastic (e large), or the industry behaves competitively
(© near zero). In contrast, if output is not responsive to changes in price when demand is
shifting out, either demand is inelastic or industry behavior is far from competitive.

Over the sample of industries with price data covering six postwar recessions since
1958, we compared the list of industries previously identified through the quantity-matching
methodology with a set of industries exhibiting a consistently low price elasticity of output.
The overlap between the resulting set of industries and those listed in Table 8 (derived over the

same sample using our quantity-matching technique) appears greater than would occur by

33To show this, we differentiate the partial equilibrium system (2) and (5) with respect to a
demand shift variable y, and we impose the restrictions Cy= ©, = @, = fyg = fyy = 0 to derive
the following equation: [(dQ/dy)/(dP/dy)][P/Q] = c/O.

For each industry in our sample for which the necessary data was available, we compute
the average output elasticity over the six months following the NEBR trough date. We require
that both price and quantity increase over this period. An industry was identified as having a
consistently low price elasticity of output if the elasticity fell between 0 and 10 for at least three
of the six postwar recessions since 1958.

26


during recessions. Kydland & Prescott (1990) show that the aggregate price level moves
countercyclically.

Our paper brings two innovations to the task of making market power inferences from
the cyclical behavior of prices. First, we distinguish the cyclical behavior of prices in stages of
the business cycle, primarily focusing on price behavior around business cycle troughs. In
contrast, previous studies have concentrated on the behavior of prices throughout the entire
cycle. Second, we employ highly disaggregated price data. In particular, we employ data
disaggregated to the two-, four-, five-, seven- and nine-digit SIC industry level in order to
allow for cyclical variation in relative prices across sectors of the economy.

We find that the cyclical behavior of prices differs between business cycle peaks and
business cycle troughs. This difference is consistent with our view that price fluctuations often
have different causes in different stages of the cycle: rising marginal costs are likely the most
important determinant of price variation around peaks,* and market power effects are likely the
most important determinant of price fluctuations around troughs. We also observe some
tendency for disaggregated prices to rise immediately following a business cycle trough.

In those industries experiencing rising industry prices around business cycle troughs, we
find that the price increases do not appear to result from marginal cost increases, that prices
tend to rise in concentrated industries, and that certain industries systematically experience price

increases across recessions. These results are consistent with either of two market power

SBils (1987) and Bresnahan & Suslow (1989a, 1989b) document the effect on prices of
sharply rising marginal costs associated with the approach of output to capacity constraints.

3


business cycle.* Many authors find that prices exhibit procyclical behavior (e.g. Bils, 1987),
and seek to explain that result in terms of cyclical regularities in cost, demand and oligopoly
behavior. The most common explanation is that price-cost margins behave procyclically in
concentrated industries (e.g. Domowitz, Hubbard & Petersen, 1986a, 1986b, 1987);* indeed,
Schmalensee (1989a) calls this observation a stylized fact. Margins, in turn, may fluctuate over
the business cycle because of cyclical variation in the elasticity of demand in many industries
(Shapiro, 1988) or because the effectiveness of firm coordination varies over the business
cycle. In contrast with explanations for the procyclical behavior of prices based on the
behavior of margins, Bils (1987) contends that prices increase during booms because short-run
marginal cost is strongly procyclical in many industries.

Whether prices actually behave procyclically, however, is far from settled. Domowitz,
Hubbard & Petersen (1987) find countercyclical pricing in high-margin producer good
industries. They interpret this result as reflecting the tendency of such industries to experience
price wars during booms. Similarly, Morrison (1993) finds negative correlations between

markups and capacity utilization, although she also observes that markups appear to decline

5Although we follow the literature in working with readily available price indices, this
common practice has been criticized. Stigler & Kindahl (1970) and Warner (1990) emphasize
the role of
measurement problems with the available price data in explaining observed price rigidities.
Carlton (1989) and Blinder (1991) show how looking solely to price fluctuations can overstate
the significance of price rigidities when competition occurs in multiple dimensions (including
delivery lags).

“Other evidence for procyclical margins not relying on price behavior is offered by Hall
(1988), Domowitz, Hubbard & Petersen (1988), and Schmalensee (1989b).

2


i. Empirical Analyses of the Cyclical Behavior of Prices

A. The Behavior of Disaggregated Prices Around the 1982 Business Cycle Trough

We begin our description of the cyclical behavior of disaggregated prices with an
examination of price movements around the 1982 business cycle trough. Because coverage of
industries in the producer price indexes has expanded over time, the focus on a recent trough
enlarges the sample substantially beyond the number of industries whose price behavior can be

examined over multiple recessions, the subject of the next section.

1, Industries Exhibiting Large Price Changes

Table 5 reports the distribution of real (deflated) price changes in the eleven months
before the November 1982 trough and the two years after.
[INSERT TABLE 5 HERE]
Most of the industries in our sample do not experience even 5% real price changes over the
one and two year pre-trough periods reported in the table. Yet of those industries that do
experience significant price changes, especially during the two years following the trough, many
more experience steeply rising prices than experience steeply declining prices. We thus find

some tendency for industry prices to rise immediately following a business cycle trough.

15


unrelated to industry-specific changes in demand, cost, or oligopoly behavior.” A large number
of industries are represented. For example, the 1982 PPI serics includes 208 four-digit
industries and 288 five-digit industries.

The most distinctive feature of our data is the great variation in price movemements
across individual industries, after controlling for aggregate price level fluctuations. This feature
is immediately evident in Figure 1, which depicts the cyclical behavior of (deflated®) prices in
the four-digit industries within the two-digit chemicals industry (SIC 28).

[INSERT FIGURE |] HERE]

{INSERT FIGURE 2 HERE]
The figure illustrates price changes between January 1982 and December 1984, a three year
time period around a business cycle trough. Each price index has been normalized to equal one
at the November 1982 trough.

As Figure | suggests, there is a great deal of variation in the prices of the four-digit
industries within each two-digit industry. This is demonstrated in Table 1, which shows the
generally low correlation of four-digit industry prices within each two digit industry during the
period between January 1982 and December 1984.

[INSERT TABLE 1 HERE]

7In contrast, Fair (1993) treats the economy-wide price level as a good proxy for the price
of a close demand substitute for every disaggregated good in the economy.

‘Figure 2 exhibits the aggregate (economy-wide) behavior of prices between J anuary 1982
and December 1984. Seasonally adjusted prices rose approximately 5% between January
1982 and December 1984. After seasonal adjustment, aggregate prices rose at a less rapid
rate after the trough than before.


cooperation (market power) rises (O, > @,).”° In both cases, market power is exercised (©,
> 0).7

Of the two possibilities--changes in shape of the demand function or changes in the
degree of cooperation, we conjecture that our quantity-matching assumption makes the latter
the more likely interpretation during the period surrounding a business cycle trough. Regardless
of the way an industry’s demand elasticity varies over the business cycle, the elasticity is unlikely
to change asymmetrically in the neighborhood of the trough, as the bust ends and the boom
begins. That is, if demand grows more elastic (say) as output declines toward the trough, we
conjecture that demand will generally grow less elastic at a similar rate as the boom begins.”* If

this conjecture is correct, industries in which price rises around a business cycle trough while

6Both interpretations of a price increase given quantity matching are consistent with the idea
that the price rise emerges from an outward shift in demand, a sensible assumption for
interpreting price changes observed as a boom commences. To see that demand likely shifts
outward, assume a linear demand function and constant marginal cost. If the solution concept
parameter is held constant, a rise in price requires that the demand function grows steeper and
its intercept rises. If instead the slope of the demand function is held constant, a price increase
implies both that the solution concept parameter rises and that the demand intercept increases.
(Had we instead adopted price matching, we would have looked for quantity reductions to
identify the exercise of market power. Such observations would be predicated on inward shifts
in demand, the opposite of what we expect to observe in the early stages of a boom.)

7 Although this nonparametric procedure does not permit us to separate the parameters of
the demand function from the solution concept parameter, we can, under the maintained
hypothesis that marginal cost is constant, identify changes in a function that is a multiple of the
solution concept parameter. In consequence, we can identify when © > 0.

28 Asymmetric expectations provide the most plausible rationale for expecting the demand
elasticity to change asymmetrically around the trough. Yet, although rising economic activity
must eventually lead firms to conclude that a recession has ended, it is difficult to see why
expectations of future demand would change in the early stages of a boom, before output has
risen above the recessionary level reached (say) six months before the trough.

21


Market Power and the Cross-Industry Behavior

of Prices Around a Business Cycle Trough

Jonathan B. Baker and Peter A. Woodward


TABLE 6

FOUR DIGIT INDUSTRIES EXPERIENCING LARGE PRICE INCREASES AND DECREASES

SIC NUMBER

2018
2044
2048
2091
2131
2253
2448
2492
2611
2631

271
2721

3
3278

3671

2074
2077
2079

ait
3211
3331

INDUSTRIES

PRODUCT

Pouttry and Egg Processing

Rice Milling

Prepared Animal Feeds (exciuding Pet Food)
Canned and Cured Seafoods
Chewing & Smoking Tobacco

Knit Outerwear

Wood Pallets and Skids
Particieboard

Pulp Mills

Paperboard Mills

Corrugated & Solid Fibe: Boxes
Newspaper Publishing

Periodical Publishing
Pharmaceutical Preparations
Leather Tanning & Finishing
Gypsum Products

Primary Zine

Primary Aluminum

Secondary Nonferrous Metals
Aluminum Sheet, Plate and Tube Products
Small Arms Ammunition

Bectric Lamps

Noncurrent-carrying Wring Devices
Lighting Equipment, N.E.C.

Bectron Tubes

Chocolate & Cocoa Products
Cottonseed Oil Mii Products
Animal Fats & Oils

Shortening & Cooking Cis
Coffee

Petroleum Refining

Rat Glass

Primary Copper

PERCENT CHANGE IN PRICE
NOVEMBER 1982 TO

November 1983 November 1984

14 10
3 7
2 2
13 2
9 6
9 u
4 13
8 10
1 10
5 1%
i) u
8 14
6 W
7 4
7 12
2 ws
13 6
nN 0
12 1
9 n
19 2
9 10
3 u
0 12
3 24
“19 an
“9 “12
-21 1
-28 9
a a
“10 “6
Cy) a
4 “12


all but one of the remaining industries, price is most highly correlated with roughly
contemporaneous quantity (no more than two periods away).

We perform a similar analysis of the degree of procyclicality of prices around six NBER
peaks: August 1957, April 1960, December 1969, November 1973, January 1980, and July
1981. These results, reported in Table 4, are closer to what was observed over the entire
business cycle. {INSERT TABLE 4 HERE]

Around peaks, as with the overall sample, price changes tend to lag output changes. In seven
industries, price changes lag output changes by five months, and in another seven industries
prices move roughly contemporaneously with output (most highly correlated with quantity no
more than two months away). Only in three industries, do price changes lead output changes
significantly.

The comparison of Table 2 with Table 3 and Table 4 demonstrates the distinct cyclical
behavior of prices during troughs -- in comparison to the behavior of prices both around peaks
and throughout the entire cycle. Both over the cycle and around peaks, price changes lag
quantity changes by five months (the maximum observable in our analysis) in a large number of
industries. In striking contrast, during troughs price changes lag quantity changes by more than
one month in only one industry. Moreover, in twelve of seventeen industries, the degree of
procyclicality of prices in the sample as a whole (Table 2) is closer to that observed when the
data is limited to the period around peaks (Table 4) than to that observed around troughs
(Table 3), and in three more the peak data and trough data are equally close in degree of

procyclicality to the overall data. Thus, the distinct procyclical behavior of prices around


seventeen (detrended) two-digit price series with the (detrended) economy-wide industrial
production index over the sample period 1948 to 1990.'° Each row of Table 2 reports the
correlation of aggregate output with eleven lime series created by leading and lagging a specific
two-digit industry’s prices up to five months.

[INSERT TABLE 2 HERE]
The highest correlation coefficient in each row is indicated by an asterisk.

For fourteen of the seventeen industries, we replicated what Kydland & Prescott found
in aggregate data: on average, aggregate output changes lead price changes. Although output
is the most highly correlated with a price at least four months in the future in most of these
industries (nine), there is substantial variation across industries in the degree of procyclicality.
Price changes lead output changes in only three industries.

To study the degree of procyclicality of prices at different stages of the business cycle,
we repeat these correlations with data limited to a five month window on either side of six
NBER postwar troughs: April 1958, February 1961, November 1970, March 1975, July
1980 and November 1982. These results, reported in Table 3, nearly reverse the pattern
observed over the entire cycle.

{INSERT TABLE 3 HERE]

Price changes now lead quantity changes by four or five months for five two-digit industries. In

‘Because inflation is a major component of the price trend, detrending makes it
unnecessary to deflate prices. We are grateful to Finn Kydland and Edward Prescott for
sharing their detrending algorithm with us.


Table 2:

Industry

20
21
22
23
24
25
26
28
29
30
a
32
33
34
35
46
v

Correlations of 2-Digit Detrended Prices with Det.

d=-5

0.02
-0.15
-0.02
-0.15

0.33

0.3
“0.22

0.2
0.03
-0.22

0.14
-0.14
-0.18
-0.32

0
-0.26
-0.09

1

in-4

0.07
-0.2
0.07
-0.09
0.34
-0.28
-0.22
-0.18
0.01
-0.17
0.17
-0.09
-0,13
-0.29
0.02
-0.27
0.05

i=-3

0.15
-0.21
0.14
-0.04
0.38
-0.26
-0.22
-0.16
0.06
-O0.11
0.19
-0.11
-0.06
~0.23
0.03
-0.25
-0.04

Correlation of Qt with Pt+i

0.2
-0,14
0.22
0.06
0.43%
-0.19
-0.12
-O.11
0.09
-0.03
0.24
-0.03
0.06
-0.15
0.04
-0.2
-0.2

de-1

0.23"
-0.11
0.29
0.12
O.42
0.09
0
-0.04
On
0.06

0.29"
0.03
0.18
-0.05
0.06
0.13
-0.26

Output series is aggregate Industrial Production Index.

rended Output in the Postwar

20

@.11
-0.16
0.33
0.15
0.31
-0.05
0.06

0.09
0.12
0.29
-0.03
0.22

0.08
-0.08
-0.2

dead

9.03
-0.16
0.35
0.18
0.16
0.04
0.09
0,04
0.07
0.16
0.26
-0.07

0.23*
0.03

o.1F
0.03
0

is+2

-0.15
0.36*
0.24
0.03
0.01
0.15
0.06
0.09
0.18
0.23

-0.06
0.22
0.07
0.09

0.22"

Period
Let? detd

0.03 0.03
-0.08 0.03
0.35 0.32
0.26 0.32
-0.03 -0.04
0.07 0.14
0.18 0.27
0.07 0.1L
o.11t o.1
0.18 0.17
0.19 0.16
-0.03 0.06
0.19 0.21

0.1 0.15
0.05 0.01
0.05 0.13
-0.01 0.17

ise

0.01
0.04"
0.27

0.33"

0.16%
0.34¢
OL 1b"
0.05
0.19
0.14
o.1lt
0.22
0.17°
0.03
O.1Be
0.05


We evaluate whether price increases over troughs correlate with concentration fora
sample of 496 industries for which four-firm concentration ratios were reported in the 1982

Census of Manufactures.*® The association between high concentration and high price change

is strong. For the subsample for which price rose by at least 6% between the reference month
and the matching month, the mean four-firm concentration ratio is 59.6%. For the sample as a
whole, the mean concentration ratio is 46%. This difference in means seems substantial,
although we cannot perform a valid significance test because price changes are correlated in
related industries, so our observations are not independent.

Table 9 demonstrates that the relationship between price changes and concentration

obtains throughout the sample.

{INSERT TABLE 9 HERE]
The industries we studied were ordered by price changes, and formed into deciles. The table
reports the mean concentration ratio for each decile. An association between higher price
increases and higher levels of concentration is evident, with the exception that the industries
experiencing the largest price declines had a higher than average degree of concentration.

The association between concentration and recessionary price increases remains when

“The reported concentration ratios are for four- and five-digit industries, so we limited our
attention to these. We excluded from our sample a small number of industries with likely local
or regional geographic markets, because within them the Census concentration ratio will likely
be misleadingly low. We also excluded a small number of industries that on average use a
particular input intensively, because output prices in these industries will be extremely sensitive
to imput price fluctuations.

28


TABLE 7: LARGE REAL PRICE INCREASES USING MATCHED OUTPUTS

SIC NUMBER PRODUCT % Change in Price
20114 Fresh and Frozen Pork 5
20116 Processed and Cured Pork 4
20130 Sausage and other Prepared Meats 8
20136 Sausage Pork 4
20137 Sausages 7
20183 Turkeys 12
20870 Chewing Gum or Gum Base 7
2087 1 Chewing Gum 6
2087 3 Soft Drink Bases 7
2092.4 Frozen Packaged Shelifish 10
2436 3 Specialty Softwood Plywood 5
2436 5 Softwood Plywood Sheathing 5
26471 Sanitary Napkins and Tampons 8
27110 Newspaper Publishing 6
aig Advertising 7
27213 Business Periodicals 10
27311 Textbook Publishing 8
28135 Nitrogen Gas 8
28312 Vaccines, Toxoids and Antigens 6
28340 Pharmaceutical Preparations 8
28341 Prescription Pharmaceutical Preparations 7
28342 Non-Prescription Pharmaceutical Preparations 6
as Leather Tanning: Rough, Crust and Wet Blue 2
33310 Primary Copper a
39312 Refined Copper 5
3333.0 Primary Zine ry
33983 Primary Nonferrous Metals, ex. aluminum and copper 9
33415, Precious Metals 65
33560 Nonferrous Metal Forming 2
33863 Precious Metal Mill Shapes 65
34335 Steel Heating Boilers 6
34820 ‘Small Arms Ammunition 7
34901 Sates and Vaults 7
34992 Collapsible Tubes 19
34993 Metal Strapping 7
35104 ‘Automotive Diesel Engines 5
35235 Farm Harvesting Machinery 6
3523.6 Farm Haying Machinery 8
35549 Paparmil Machinery 5
35551 Lithographic Printing Presses 7
36767 Scale and Balance Accessories 5
36812 ‘Automatic Merchandising Machines Parts a
3505 4 Refrigerator and Heating Compressors 6
38194 Fuses, Equipment, Under 2300 Volts 7
38212 Intagral Motors and Generators 8
36313 Gas Household Units and Parts 6
36710 Etectron Tubes 13
36711 Receiving Electronic Tubes, except Cathode Ray 6
36713 Transmitting and Power Tubes 8
36783 Variable Resistors 8
36785 Fixed Resistors 5
3m12 Trucks, Truck Tractors, and Chassis 7
38221 Building Environment Controls 6
38251 Electrical integrating Instruments "
38411 Surgical and Medical instruments 5
30110 Jewelry W
ant Platinum and Gold Jewelry 20
30142 Mota Flatwore and Carving Sets 4
39150 Jewolers' Materials 8
39151 Jewolers Findings and Materials v7


chance We conclude that the output elasticity methodology and the quantity-matching
methodology select for the same industry characteristics. This observation suggests the
difficulty of disentangling the two market power explanations for the increase in prices as a
boom began -- a more inelastic demand curve for industries currently exercising market power,

and greater coordination.

Cc. Characteristics of Industries Exercising Market Power

To the extent the industries that increase prices over recessions are on average more
likely to exercise market power than most, as we have argued, we can learn from their
characteristics about the structural features of industries likely to perform poorly. In this
section, we focus on two characteristics: industry concentration and import penetration. We
find a positive association between price increases over troughs and concentration. We also
find that industries in which prices increase over troughs tend to be free from import

competition. We conducted this analysis with data from the 1982 recession.

1. Concentration

35The unconditional probability of an industry appearing in Table 8 is 13% (22/166), while
the probability of an industry appearing in Table 8 given that it also has a consistently low price
elasticity of output is 23% (7/30).

27


explanations for rising industry prices over troughs: oligopolists pricing above marginal cost
may be able to take advantage of a cyclical tendency for demand functions to grow more
inelastic in the early stages of a boom, or else interfirm coordination may become more effective

after a trough.

I. The Importance of Disaggregating Prices

This section demonstrates that aggregate price series mask substantial variation across
disaggregated industries, including differing patterns of cyclical behavior. This observation
suggests that industry-specific factors are important determinants of the cyclical behavior of

prices.

A. Inter-Industry Price Variation

Our data on prices are taken from the monthly Producer Price Indexes® (PPI), and are
not seasonally adjusted. These data include prices for two-, four-, five-, seven- and nine-digit
SIC industries, 1948 to 1990. We deflate the disaggregated series by the aggregate PPI on the

view that variation in the economy-wide price level can be attributed to macroeconomic forces

All tables, graphs, and empirical work in this paper involving prices take these from the
PPI.


utilized shortly after the commencement of the boom.

It is not surprising that cost increases largely fail to explain the price increases observed
in the first year or two following a business cycle trough. Outside of business cycle peaks, most
industries are likely to experience roughly constant returns to scale for normal fluctuations in
output,” either because the firms find it best to adopt a production technology that does not
strongly penalize output variations from efficient scale or because they smooth production in
response to anticipated demand variation. Because cost explanations for the price increases in
the industries listed in Table 6 are implausible, we believe that in most cases the observed price
increases reflect the exercise of market power. Market power explanations are of two sorts.
Either demand grows less elastic (in an industry exercising some market power), or interfirm
cooperation improves (while industry demand is not perfectly elastic). The next section
explains our nonparametric method of controlling for changes in demand elasticity, in order to

identify industries which tended to grow more cooperative as the boom began.

2. Industries with Increasing Margins as the Boom Began

We next develop a second list of industries characterized by rapid price increases

2One exception: oil services industries may have experienced capacity constraints while the
rest of the economy was mired in the two post-oil shock recessions of the late 1970s.

2Four-digit SIC industries are likely in general to be sufficiently free of competition from
substitute products sold by other industries to permit the exercise of market power. Pittman
and Werden (1989) and Werden (1988) conclude that the minimum agglomeration of products
necessary to permit successful collusion js even narrower than four-digit industries.

18


26.

27,

28.

29,

30.

31.

32.

36.

37.

38.

39,

Porter, R., 1983, "A Study of Cartel Stability: The Joint Executive Committees
1880-1886," Bell Journal of Economics, 14, 301-314.

Porter, R., 1985, "On The Incidence and Duration of Price Wars," Journal of Industrial
Economics, 33, 415-426.

Prescott, E., 1986, "Theory Ahead of Business Cycle Measurement," Carnegie-
Rochester Conference Series on Public Policy, 25, pp. 11-44.

Rotemberg, J. and Saloner, G., 1986, "A Supergame Theoretic Model of Price Wars
During Booms," American Economic Review, 76, p. 390-407.

Schmalensee, R., 1989a, "Inter-Industry Studies of Structure and Performance," in
Handbook of Industrial Organization, R. Schmalensee and R.D. Willig, (eds.), p.
951-1009.

Schmalensee, R., 1989b, "Intra-Industry Profitability Differences in US Manufacturing
1953-1983," Journal of Industrial Economics, 37, pp. 337-57.

Shapiro, M., 1988, "The Cyclical Behavior of Price-Cost Margins: Demand Elasticity
and Marginal Cost,” unpublished manuscript, January.

Shea, J., 1993, "Do Supply Curves Slope Up?" Quarterly Journal of Economics, 108,
1-32.

Shepherd, W.G., 1982. "Causes of Increased Competition in the U.S. Economy,
1939-1980," The Review of Economics and Statistics, 64, pp. 613-26.

Shepherd, W.G., 1981. "Technical Appendix," unpublished manuscript.

Stigler, G. and Kindahl, J., 1970, The Behavior of Industrial Prices.

Suslow, V., 1988, "Stability in International Cartels: An Empirical Survey," Hoover
Institution, Stanford University Working Paper No. E-88-7.

Warner, E., 1990, “The Behavior of Retail Prices over the Seasonal Cycle,"
unpublished manuscript, May.

Werden, G., 1988, "The Divergence of SIC Industries from Antitrust Markets: Some
Evidence from Price Fixing Cases," Economics Letters, 28, pp. 193-97.




APPENDIX: Correspondence Between the Price and Quantity Indices

Industrial Production
Index Code

20100
20220
20230
20240
20300
20400
20410
20500
20701
20820
20840
20850
20860
20901
21110
21211
21000
22100
22210
22333
22510
22590
22600
22700
22809
23000
24000
24300
24309
24500
25100
25900
26000
27000
28000
29000
30000
31000
32000
33129
33000

Description

Meat Products

Cheese

Concentrated Milk
Frozen Desserts
Canned and Frozen Foods
Grain Mill Products
Flour

Bakery Products

Fats and Oils

Beer and Ale

Wine and Brandy
Liquors

Soft Drinks

Coffee and Misc. Foods
Cigarettes

Cigars

Tobacco Products
Cotton Fabrics
Synthetic Fabrics
Fabrics

Hosiery

Knit Garmets

Fabric Finishing
Carpeting

Yam & Misc.

Apparel Products
Lumber and Products
Millwork and Plywood
Lumber Products
Manufactured Homes
Household Fumiture
Fixtures and Office Fum.
Paper and Products
Printing and Publishing
Chemicals and Products
Petroleum Products
Rubber and Plas. Products
Leather and Products
Clay, Glass, and Stone Prods.
Iron and Steel

Primary Metals

Corresponding Producer
Price Index Codes

2011-2018, .
2022
2023, 2026
2024
2032-2038,
2043-2048
2041
2051-2067
2074-2079
2082-2083
2084
2085
2086
2091-2099
211
2121
2131
2211
2221
2231
2251-2252
2253-2258
2261-2262
2272
2281-2298
2311-2396
2411-2421
2431-2439
2448-2449, 2492
2451-2452
2511-2515
2521-2522
2611-2655
2711-2731
2812-2899
2911-2992
3011-3079
3111-3172
3211-3296
3312
3315-3369


section identifies such industries and investigates their characteristics.

1. Industries with Increasing Margins as the Boom Begins

If either oligopoly behavior or the slope of demand functions varies predictably over the
business cycle for many industries, and if many industries have not changed significantly in
market structure during the last several decades, we should observe some industries exhibiting
consistent patterns of steep price increases during recessions. The sample we used to study
whether this conjecture holds incorporates 177 four-, five-, seven- and nine-digit industries for
which consistent price and output data was available over the 1958, 1961, 1970, 1975, and.
1982 recessions.** We identify reference and matching months in the same way here as for our
study of the 1982 trough.

Table 8 reports those products which exhibited a (real) price increase in at least five of
the six recessions studied.

{INSERT TABLE 8 HERE]
We interpret this list as collecting industries more likely than most to have experienced the
exercise of market power. These industries experienced cooperative pricing as booms began,
or else they exercised a constant degree of market power while typically experiencing steeper

demand as booms began.

We excluded the 1948 and 1954 recessions because most of the output series begin in
1954.

24




TABLE 5

THE DISTRIBUTION OF LARGE INDUSTRY PRICE CHANGES AROUND THE 1982

BUSINESS CYCLE TROUGH
Percent January 1982- November 1982- November 1982-
Change in Price November 1982, November 1983, November 1984,
Decreases
-10% or more 5 6 6
-5% or more 18 20 19
Increases
+5% or more 19 32 50
+10% or more 2 9 19

Number of four-digit SIC industries is 208.
Data is deflated, not seasonally adjusted.


:

i

Table 3:

Correlations of Detrended 2-Digit Price

-5 in-4
o.1a* * 0.06
-0.08 0.07
0.43* 0.4
0.21 0.25
-0.13 -0.34
0.09 0.12
0.14 0.22
0.05 0.13
0.16 0.21
0.13 0.14
0.16* 0.08
0.02 0.06
0.37* 0.29
0.08 0.11
0.03 0.04
-0.03 0.02

0.1 0.31¢

4

-0.07
0.13
0.3
0.26
-0.35
0.17
0.31
0.16
0.26
0.15
-0.06
0.11
0.24
0.17
0.05
0.07
0.11

Correlation of Qt with Pt+i

ie-]2

-0.17
0.18*
0.22
0.36*

-0.29
0.25*
0.45
0.23
0.28*
0.17
-O0.1
0.22
0.32
0.29
0.08
0.16
0.24

4a-1
-0.19 -0.2
0.13 0.2
0.11 -0.01
0.36 0.2
-0.2 -0.1
0.22 0.15
0.48* 0.39
0.33 0.35*
0.22 O.1
0.2 0.18*
-0.03 0
0.3 0.29
0.34 0.31
0.35 0.36*
O.1 0.14
0.22* 0.2
0.15 0.02

Output series is aggregate Industrial Production Index.

with Detrended Output in

d=el

-0.1
0.08
-0.06
0.15
-0.04
0.1
0.32
0.31
-0.09
0.15
0.06
0.34*
0.29
0.31
0.16*
0.14
-0.06

0.01

0.05
-0.17
-0.12

0.02

0.02*

0.08

0.06

0.13
-0.25

0.07
-0.06

0.08

0.05

0.2

0.12

0.08
-0.08

wt

0.14
-0.16
-0.12

-0.01
0.07
0.05
0.11

-0.22
0.06

-0.11
0.07
0.05

0.2
0.11
0.13

-0.18

the Postwar Period Around NBER Troughs

=+5

0.08
-0.17
-O.11
-0.02
-0.13

0.1

0.05

0.07
-0.12

0.07
-0.14
-0.02

0.09

0.23

0.09

0.15
-0.13


capacity for many firms. Marginal cost is high at high output levels because firms pay overtime
rates to labor, expand operations at less efficient plants, and otherwise pay a high price to meet
demand. (Bils, 1987; Bresnahan & Suslow, 1989b).!* In contrast, excess capacity is common
and overtime payments are reduced in the months surrounding a cyclical trough.

Second, demand might grow more elastic around a trough, leading to a reduction in an
industry’s price-cost margin if the firms are exercising some market power. For example, the
demand for some consumer products might be elastic at troughs, if buyers limit consumption in
order to build a reserve of savings against the threat of job loss.

Third, a number of models of oligopoly behavior imply that firms will have difficulty
coordinating during recessions. For example, large negative demand shocks may induce firms
to act competitively for a time, before returning to cooperative pricing. (Green & Porter, 1984)
This story suggests that industry prices will be higher after a trough than before, because
negative demand shocks causing competitive pricing are less likely to occur in a recovery
period than during a recession. Similarly, when demand shocks have positive serial correlation,
firms will likely find it more difficult to collude during recessions than during booms. During
recessions the foregone profits from inducing competitive pricing will be relatively low, so,

holding the level of demand constant, price will be lower when demand is declining than when it

Even if capacity is added in discontinuous (lumpy) increments, or firms build inventory to
smooth production in anticipation of predictable fluctuations in demand, marginal cost would
likely be low on average during troughs. Because firms would plan to add capacity and
produce at full capacity when demand is both high and likely to increase, excess capacity would
be the greatest, and marginal cost the lowest, during periods of low or declining demand.

11


to keep employment high when demand and production are low, in order to avoid losing
permanently those workers trained in firm-specific production techniques. Consistent with this
story, average manufacturing hours are strongly procyclical (Bils, 1987). Thus, to the extent
firms adopt a production technology that penalizes output declines from optimal scale, marginal
cost would tend to rise systematically during recessions.'®

Second, demand might grow more inelastic at troughs, leading producers exercising
some market power to raise price. For example, demand for some consumer products might
be inelastic at troughs if liquidity-constrained consumers are out of the market and those not
constrained have the most inelastic demand. Or, consumers may spend less effort on search for
the lowest price at times when they are buying less, such as during recessions (see Barsky and
Warner (1993)), making demand more inelastic around troughs. Demand might also be
inelastic during recessions for products whose buyers bear significant switching costs, if most
customers in the market at troughs are old customers who cannot switch to substitutes without
penalty (Klemperer, 1992 §4.5). Demand for intermediate goods might be the most inelastic in
the early stages of a boom, if recognized as such, as firms seek to build inventories in

anticipation of high future demand.”

'6Two theories of macroeconomic fluctuations also suggest that firms would may to have
lower production costs in booms than recessions. Hall (1991) contends that agglomeration
economies may outweigh congestion diseconomies during booms. Supply-side theories of
macroeconomic fluctuations, such as real business cycle models (Prescott 1986), may also
generate this prediction.

'7On the other hand, demand for durable consumer goods and capital goods sold to
producers may increase when buyers anticipate higher future prices and decline when they
anticipate lower future prices in ways that smooth price variation and purge prices of any

13


ABSTRACT

Our paper examines the behavior of prices in a large number of highly-disaggregated
industries around the trough of the business cycle. We conclude that the degree to which prices
are pro- or counter-cyclical differs between business cycle peaks and business cycle troughs,
and that the cyclical behavior of prices varies substantially across industries. We also observe a
tendency for industry prices to rise immediately following a business cycle trough. In general,
we accept a market power explanation for that observation: either oligopolists pricing above
marginal cost take advantage of a cyclical tendency for demand functions to grow more
inelastic in the early stages of a boom or else interfirm coordination becomes more effective
after a trough. From the behavior of prices as a recession ends and a boom begins, our paper

also identifies a set of industries likely on average to be exercising market power.


14.

16.

18.

19.

21.

22.

23.

24.

25.

Domowitz, I, Hubbard, R., and Petersen, B., 1988, "Market Structure and Cyclical
Fluctuations in U.S. Manufacturing," Review of Economics and Statistics, p. 55-66.

Domowitz, I., Hubbard, R., and Petersen, B., 1986b, "The Intertemporal Stability of
the Concentration-Margins Relationship,” Journal of Industrial Economics, 35, p.
13-34.

Domowitz, I, Hubbard, R., and Petersen, B., 1987, "Oligopoly Supergames: Some
Empirical Evidence on Prices and Margins," Journal of Industrial Economics, 35, p.
379-398.

Fair, R., 1993, "Inflationary Expectations and Price Setting Behavior," Review of
Economics and Statistics, 75, 8-18.

Green, E. and Porter, R., 1984, "Noncooperative Collusion Under Imperfect Price
Information," Econometrica, 52, p. 87-100.

Hall, R., 1988, "The Relationship Between Price and Marginal Cost in U.S. Industry,"
Journal of Political Economy, 96, 921-947,

Hall, R., 1991, "Noise Over Space and Time," in Booms and Recessions ina Noisy
Ecoonmy (The Okun Lectures).

Haltiwanger, J. and Harrington, J., 1991, "The Impact of Cyclical Demand Movements
on Collusive Behavior," Rand Journal of Economics, 22, p. 89-106.

Klemperer, P., 1992, "Competition When Consumers Have Switching Costs: An
Overview," Discussion Paper No. 704, Centre for Economic Policy Research, July.

Kydland, F. & Prescott, E., 1990, "Business Cycles: Real Facts and a Monetary
Myth," Federal Reserve Bank of Minneapolis Quarterly Review, 3-18.

Lebow, D., 1992. "Imperfect Competition and Business Cycles: An Empirical
Investigation," Economic Inquiry, 30, pp. 177-93.

Morrison, C., 1993. “Productive and Financial Performance in U.S. Manufacturing
Industries: An Integrated Structural Approach," Sourthern Journal of Economics, 60,
376-92.

Pittman, R. & Werden, G, 1990, "The Divergence of SIC Industries from Antitrust
Markets: Indications from Justice Department Merger Cases," Economics Letters, 33,
283-286.


quantity does not are not only on average industries in which market power is being exercised;
they are also on average industries in which the degree of cooperation increases as the boom
begins.

To implement our quantity-matching algorithm, we must first choose a date for the
trough. One possibility is to assume that the business cycle trough occurred in the identical
month for every industry, the NEBR date of November 1982. We choose instead to identify
the trough endogenously with industry output data, in order to ensure that we have truly found a
trough. Another implementation issue involves identifying the matching month. We might have
chosen that month over the next two years in which the quantity sold was closest to the quantity
sold in the reference month. We preferred to select the month in which sales first rose above
the quantity sold during the reference month, in order to keep the matching month close to the
trough.”

In order to apply this algorithm, we must have quantity data corresponding to our price
data. Our output information, the Industrial Production indexes reported by the Federal
Reserve, is reported in broader aggregates than the price data, often much broader. In
consequence, the applicability of our quantity matching technique frequently turns on the

assumption that each narrowly-defined industry output measure we cannot observe follows a

2°Although the four possibilities implicit in these choices yield virtually the same set of
industries, we used the "endogenous trough" definition combined with the "first crossing"
definition of the matching
month, since these definitions are the closest to our view of the business cycle.

%°In an appendix, we report the correspondences we employ between industrial production
indexes and price indexes.

22




TABLE 8
FREQUENT PRICE INCREASES USING MATCHED OUTPUTS: 6 POSTWAR RECESSIONS

RECESSION TROUGH:

INDUSTRIES PERCENT CHANGE IN PRICE #OF
SIC NUMBER PRODUCT 1958 1961 1970 1975 1980 1982 MEAN INCR.
2844 P Toilet Preparations 1 1 0 1 3 1 1 8
3221131 8 3 8 10 2 1 5 6
3423 P Hand & Edge Tools 5 2 3 7 ’ 3 4 6
3423 116 ‘Screw Drivers 4 2 0 4 3 2 3 6
3533 362 & Gas Field Equipment - Packers 8 8 5 2B 2 8 9 6
2011 63101 Ham: Process & Cured (not canned) 8 a 14 5 2 18 4 5
2077 21111 ‘Animal Meat, Meat & Bone Meal 2 18 3 5 4a 1 10 5
2084 11421 Red Wine: Generic, Semi-generic, 13 1 ci) 2 5 1 a 5

Proprietary
2511 331 Wood Dining Room Chairs 2 2 1 2 1 0 1 5
2511 951 Wood Buffets & Servers, Dining Room 4 1 1 2 0 1 1 5
3221117 iquor Glass Containers 5 a 8 6 2 4 4 5
325111107 Building or Common Brick 0 1 3 8 a 3 2 5
3261 117 China & Earthenware Lavatories a 0 2 10 3 2 3 5
3312 324 Blackplate - Tin Mill Products 3 a 8 13 1 3 4 5
3423111 Piers 3 5 1 7 3 A 3 5
3423 321 Files 2 ’ 0 4 2 3 1 5
3433. 313 Gas Fired Cast iron Heating Boilers 0 0 5 8 2 1 2 5
3623 0 Farm Machinery & Equipmer 0 1 2 12 ci) 4 3 5
3523 316 lanting Equipmer 1 2 5 13 4 4 4 5
3523 90083 Farm Machinery & Equipment Parts A 2 3 3 2 6 3 5
3592 P Mining Machinery & Equipment 6 2 1 a 1 0 8 5
3962 417 Mounted Ball Bearings 3 1 4 v7 4 4 4 5
3566 0 ‘Speed Changers, Industrial Highspeed Drives. tt) 3 2 8 1 a 2 5

& Gears
3568 228 ‘Speed Reducers Small - Low Speed Centers 5 1 ? 35 4 1 8 5
3613 P ‘Switchgear and Switchboard Apparatus 1 aot 5 8 3 2 a 5
3621 0 Electric Motors & Generators 0 5 2 16 1 7 3 5
3622 P Industrial Controls 5 0 7 2 4 3 4 5
3622 11204 Noncombination, Full Voltage Starters 1000. orless 6 0 8 14 4 4 5 5
3622 13 ‘AC Contactor 1000V or less 4 0 7 “4 4 1 4 5
3861 P Photographic Equipment & Supplies 1 2 0 3 15 1 3 5


manufacturing average of 11.1%. The correlation of price increases with low import
penetration is consistent with the view that foreign competition tends to make domestic

industries more competitive.

Dz. Comparison with Other Methods of Identifying Industries Exercising Market

Power

We compared the industries we identified as on average likely to be exercising market
power with the lists of industries exercising market power derived independently by two other
authors in order to evaluate our view that industries raising price around troughs are likely
exercising market power. We chose these two studies for an intensive comparison because
each classifies industries at the four-digit SIC level. The results are mixed: we find substantial
overlap between our results and the results of applying one of these different methodologies, but
no significant overlap between our results and the results of applying the second method.

Shepherd (1981, 1982) evaluates the market power exercised in all four-digit SIC
industries (approximately four hundred fifty”) in 1980 from studying firm market shares,

concentration ratios, entry barriers, pricing behavior, profitability, and innovation.* He

We are grateful to Geoffrey Shepherd for providing this estimate of the number of
industries he analyzed.

38Although Shepherd takes pricing behavior into account in making his classifications, his
subjective analysis is largely based on factors other than the behavior of prices during
recessions. We therefore treat his methodology as employing information independent of the
data we study.

30


TABLE 1

Price Dispersion Within 2-Digit Industries

Number of

INDUSTRY Industries R-Squared
20 Food and Kindred Products 4 0.06 01S
21 Tobacco Manufactures a 0.69 0.57
22 Mill Products. 13 0.42 041
23 4 0.24 0.10
24 lumber and Wood Products 7 0.44 0.42
25. Furniture and Fixtures 6 oat 0.59
26 Paper and Allied Products 6 0.58 0.42
27 Pi ang Publishing 3 0.99 0.98
28 Chemicals and Allied Products 12 0.22 0.20
29 leu Retining 4 on 0.51
30 Rubber and Plastic Products 4 0.26 0.25
21 Leather and Leather Products 5 0.28 0.40
32 Stone, Clay and Glass Products 16 oat 0.73
33. Primary Metals: v 0.36 0.33
34 Fabucated Metal Products 4 0.09 0.28
35. Nonelectrical Machinery 23 047 . 0.43
36 Electrical Machinery 1 0.40 0.56
37 Transportation Equipment 3 oat 0.33

'

January 1982 to December 1984.
Not Seasonally Adjusted


The industries reported in Table 8 are distributed across two-digit industries far
differently from their distribution across the entire sample of 177 firms. The most
over-represented two-digit industries include Stone, Clay and Glass Products; Fabricated
Metal Products; Nonelectrical Machinery; and Electrical Machinery. The most
under-represented two-digit sectors include Food and Primary Metals.

Our interpretation of equation (9) suggests that systematic price increases around
troughs in particular industries are indicative of market power. One alternative hypothesis is
that price increases in one recession are randomly distributed among industries, and that the
industries reported as experiencing price increases in multiple recessions do so at random. We
tested this hypothesis by comparing the expected distribution of price increases under the
hypothesis of randomness with the observed distribution, and rejected the alternative of

randomness at a 5% significance level.

2. The Elasticity of Output as the Boom Begins

We compared the industries identified through our quantity-matching methodology with
those selected using a different though related method of identifying industries likely on average
to exercise market power. The alternative algorithm selects those industries with the lowest
price elasticity of output -- the ratio of the percentage change in output to the percentage
change in price -- during the first six months following a business cycle trough.

To interpret the price elasticity of output, we assume that during the period just

25


troughs is masked in the overall data, which primarily reflects the countercyclical behavior of

prices around peaks.

Ti. Interpreting the Cyclical Behavior of Disaggregated Prices: Theory

To interpret the cyclical behavior of prices, we adopt an analytic framework proposed
by Bresnahan (1982). Although the framework takes a static partial equilibrium perspective,
we have included expected future output in the demand and supply functions to reflect
intertemporal substitution by buyers or sellers in response to anticipated cyclical economic
fluctuations.

Industry demand at time t is
(2) P.= f(Q,y,Q) + vi
where P is the market price, Q is quantity sold, y represents a vector of demand shift variables
such as income, Q* represents expected future quantity, f(Q,,y,,Q*) is the demand function, and
v is arandom shock to demand. From (2), industry marginal revenue takes the form
G3) MR, = £(Q,,y.Q°) + vi + LQ
where f, denotes the output derivative of demand. Industry marginal cost is given by
(4) MC, = c(Q,w,,Q') + u,
where c(Q,,w,,Q‘) is the cost function, w is a vector of factor prices, and u is a random cost
shock. Firms in the industry will set perceived marginal revenue to marginal cost, so their

behavior will be described by the intersection of the quasi-supply function (5) with the demand


TABLE 9
THE RELATIONSHIP BETWEEN PRICE CHANGE AND CONCENTRATION, BY PRICE INCREASE

Decile Price Ratio Range Mean Price Ratio Four Firm Concentration Ratio
1 795-950 908 47
2 951-378 966 39
3 979-993 987 44
4 994-1000 997 43
5 1001-1006 1003 44
6 1007-1014 1011 44
7 1015-1022 1019 50
8 . 1023-1033 1028 43
9 1034-1056. 1045 47
10 1057-1670 nid §9

Concentration values are taken from the 1982 Census of Manufactures.


TABLE 11
THE SHARE OF IMPORTS IN A SAMPLE OF HIGH
PRICE INCREASE INDUSTRIES

sic # PRODUCT IMPORT SHARE
—_USSHIPMENTS.
20114 FRESH AND FROZEN PORK 2.88%
20116 PROCESSED AND CURED PORK 0.10%
20670 CHEWING GUM OR GUM BASE 1.46%
28312 VACCINES, TOXOIDS AND ANTIGENS 15.24%
28340 PHARMACEUTICAL PREPARARTIONS 0.25%
33330 PRIMARY ZINC : 43.55%
34991 SAFES AND VAULTS 5.19%
34992 COLLAPSIBLE TUBES 411%
35194 AUTOMOTIVE DIESEL ENGINES 3.76%
35235 FARM HARVESTING MACHINERY 0.41%
35236 FARM HAYING MACHINERY 7.54%
36812 AUTOMATIC MERCHANDISING MACHINE PARTS 8.41%
35854 REFRIGERATOR AND HEATING COMPRESSORS 0.00%"
36710 ELECTRON TUBES 0,00%*
38251 ELECTRICAL INTEGRATING INSTRUMENTS 0.57%
38411 SURGICAL AND MEDICAL INSTRUMENTS 3.50%
39110 JEWELRY 0.72%
39142 METAL FLATWARE AND CARVING SETS 27.32%
OVERALL MANUFACTURING 11.10%
“CLOSE TO ZERO

The data used to generate this table was found in two sources.
Domestic production data was taken from The Census of
Manufactures, Industries Series - 1982. Table 6A (Product and
Product Classes - Quantity and Value of Shipments by all

producers: 1982 and 1977). In order to obtain the corresponding
import numbers, SIC-based data from this early date had to be
‘translated’ into the appropriate code, in this case TSUSA(Tariff
Schedule-United States of America) numbers. This was done with
the use of US Foreign Trade Statistics, Classification and Cross
Classifications, 1980. Usually, a single 5 digit SIC product(s)
consisted of several different TSUSA codes, The TSUSA numbers
were found using COMPRO, an on line system which reports various
trade statistics for US trading partners. Tha overall percentage

‘of imports/domestic products was derived from Business

Statistics: 1961-1988. Domestic production was found on page 11,
fisted as "Shipments-Manufacturing Sales”. The quantity of
imports was found on page 81 under "Value of Imports”.


is rising (Haltiwanger & Harrington, 1991).'*

These oligopoly models provide a clear prediction about the variation in industry
markup around a recessionary trough: oligopolies exercising market power are more likely to
obtain a higher average markup during the early stage of a boom than during the final stage of a
bust. Just before the trough of a recession, unexpected demand shocks will typically be
negative, so coordinating firms will revert frequently to competitive pricing. In contrast, just
after the trough unexpected demand shocks will typically be positive, so oligopolists playing a

non-cooperative supergame will more likely achieve cooperative pricing.'®

B. Explanations for Countercyclical Price Behavior

Other theories about the cyclical behavior of marginal cost, the elasticity of demand,
and oligopoly behavior suggest instead that prices should fall at business cycle peaks or rise at
troughs. First, marginal cost might rise during recessions if producers systematically forego

scale economies during troughs in response to demand declines. For example, firms may prefer

Similarly, when consumers bear significant switching costs, and cyclical demand variations
primarily reflect changes in the demand per consumer (rather than fluctuation in the number of
new buyers), firms may behave more competitively in troughs. Here firms lower prices in order
to build market share when it is cheapest to do so, in anticipation of raising prices to locked-in
buyers in the ensuing boom. (Klemperer, 1992 §4.5)

The limited empirical evidence favors the view that when coordination is not a dominant
strategy over the entire business cycle, episodes of static collusive behavior more often break
down during busts than booms (Porter, 1983, 1985; Suslow, 1988; Baker, 1989; Baker,
Johnston & Woodward, 1990).

12