DEMAND-SIDE DETERMINANTS OF PRICE-COST MARGINS IN U.S.
MANUFACTURING
Juan Pablo Vazquez
B.B.A., Baylor University, Waco, 2003
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
ECONOMICS
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2012
DEMAND-SIDE DETERMINANTS OF PRICE-COST MARGINS IN U.S.
MANUFACTURING
A Thesis
by
Juan Pablo Vazquez
Approved by:
__________________________________, Committee Chair
Craig Gallet
__________________________________, Second Reader
Terri Sexton
____________________________
Date
ii
Student: Juan Pablo Vazquez
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
_________________________, Graduate Coordinator
Kristin Kiesel
Department of Economics
iii
___________________
Date
Abstract
of
DEMAND-SIDE DETERMINANTS OF PRICE-COST MARGINS IN U.S.
MANUFACTURING
by
Juan Pablo Vazquez
This thesis studies the determinants of price-cost margins for U.S. manufacturing
industries in the periods between 1967 and 1992 using SIC labeled industries and 19972002 using NAICS labeled industries. Aside from investigating the relationship between
industry concentration and profit margins, which has a long history of empirical research,
this thesis addresses demand-side determinants of price-cost margins using several
interactive terms in the empirical model. This thesis differentiates itself from past
research by looking at several measures of industry concentration. In addition to not
controlling for panel effects, we also consider fixed and random effects versions of the
two models used in this study. Our main findings are that the various industry
concentration measures affect profit margins positively, and that generally, price-cost
margins are pro-cyclical with respect to industry-specific demand changes, and countercyclical with respect to aggregate demand changes.
_______________________, Committee Chair
Craig Gallet
_______________________
Date
iv
ACKNOWLEDGMENTS
I would like to thank Professor Craig A. Gallet for his help throughout the thesis writing
process and for always being available to answer questions and provide guidance.
I would also like to thank Professor Terri A. Sexton for her assistance and suggestions
during the writing of this thesis.
Finally, I would like to thank the Economics faculty at California State University,
Sacramento for their dedication in the success of all students.
v
TABLE OF CONTENTS
Page
Acknowledgments......................................................................................................... v
List of Tables ............................................................................................................. vii
List of Figures ........................................................................................................... viii
Chapter
1. INTRODUCTION ..........………………………………………………………… 1
2. LITERATURE REVIEW ....................................................................................... 4
3. EMPIRICAL MODEL AND DATA .................................................................... 12
3.1 Empirical Model .............................................................................................12
3.2 SIC Data..........................................................................................................14
3.3 NAICS Data ....................................................................................................22
4. ESTIMATION RESULTS .................................................................................... 25
4.1 Table 4.1 Estimation Results ..........................................................................26
4.2 Table 4.2 Estimation Results ..........................................................................32
4.3 Table 4.3 Estimation Results ..........................................................................35
4.4 Table 4.4 Estimation Results ..........................................................................38
4.5 Table 4.5 Estimation Results ..........................................................................41
5. CONCLUSION ......................................................................................................43
5.1 Summary of Findings .....................................................................................43
5.2 Suggestions for Future Research ....................................................................45
References ................................................................................................................... 46
vi
LIST OF TABLES
Tables
Page
3.1 Variable Definitions ...............................................................................................15
3.2 Descriptive Statistics (SIC) ....................................................................................16
3.3 Correlation Matrix (SIC)........................................................................................17
3.4 Price-Cost Margins by Concentration Quintile (SIC) ............................................17
3.5 Descriptive Statistics (NAICS) ..............................................................................23
3.6 Correlation Matrix (NAICS) ..................................................................................24
4.1 Results Incorporation Four-Firm Concentration (SIC) ..........................................27
4.2 Results Incorporating Eight-Firm Concentration (SIC) .........................................31
4.3 Results Incorporating Herfindahl Index (SIC) .......................................................34
4.4 Results Incorporating Interpolated Four-Firm Concentration (SIC) .....................37
4.5 Results Incorporating Four-Firm Concentration (NAICS) ....................................40
vii
LIST OF FIGURES
Figures
Page
3.1 Average Price-Cost Margins ..................................................................................18
3.2 Low-Range Price-Cost Margins ............................................................................19
3.3 Mid-Range Price-Cost Margins .............................................................................19
3.4 High-Range Price-Cost Margins ............................................................................20
3.5 Industry Concentration and Price-Cost Margins....................................................21
3.6 Cyclical Behavior of Price-Cost Margins ..............................................................22
viii
1
Chapter 1
INTRODUCTION
The percentage markup of price over the price that would be charged in a
perfectly competitive market (i.e., the price-cost margin) is measured as (P – MC)/P,
where P denotes price and MC denotes marginal cost. It equals zero in the case of a
perfectly competitive market.1 As such, since its theoretical minimum value is zero, the
price-cost margin (also labeled the profit margin) is taken as the conventional indicator of
the degree of market power in that it measures the ability of firms to price above marginal
cost, and thus serves a useful purpose in regards to antitrust deliberations.
The empirical literature on price-cost margins has focused on two broad
categories with respect to its determinants. The first category of studies focuses on
industry concentration as a determinant for profit margins.2 Since theory suggests firms
in highly concentrated markets are able to charge a higher price to consumers, there is
abundant empirical research on the relationship between price-cost margins and seller
concentration, with the typical finding that industry concentration has a significantly
positive impact on price-cost margins. The second category of studies has focused on
demand-side determinants of price-cost margins. Typically, results from these studies
have been that price-cost margins are pro-cyclical in nature. In other words, as demand
Since profit (π) = P*Q – TC(Q), and for a perfectly competitive firm price is taken as
𝛿𝜋
given, the first-order condition for maximizing profit yields 𝛿𝑄 = P-MC = 0, thus
1
implying a value of zero for the price-cost margin.
2
Industry concentration refers to the number and size distribution of firms in a given
industry. In general, highly concentrated industries have fewer firms, with each firm
having a larger market share.
2
in the market increases, this leads to an increase in profit margins (e.g., see Domowitz,
Hubbard, and Petersen (1986)).
The main objective of this thesis is to explore the effects of demand-side
determinants, as well as different measures of firm concentration, on profit margins in
U.S. manufacturing. This thesis differs from previous studies primarily through its use of
various measures of industry concentration. For the most part, the past literature has
focused on the use of the four-firm concentration ratio.3 While we initially investigate
the relationship between profit margins and four-firm concentration using SIC (Standard
Industrial Classification) industry data, we also use data on the eight-firm concentration
ratio and the Herfindahl index to assess the extent to which results are sensitive to the
measure of concentration. Furthermore, we interpolate observations of four-firm
concentration to explore whether additional observations influence results. Finally, a
newly constructed panel data set of manufacturing industries is used to analyze the
relationship between profit margins, concentration, and demand using NAICS (North
American Industry Classification System) industry data, which replaced the SIC system
in 1997.
Similar to past studies, we find that industry concentration, regardless of how it is
measured, has a strong positive effect on price-cost margins. With regard to demand-side
determinants, we find that intra-industry demand changes have a positive impact on
price-cost margins. This indicates that, as demand grows for the goods within a specific
industry, firms in that industry will experience an increase in their profit margins.
3
The four-firm concentration ratio measures the percent of the market controlled by the
four largest firms in a given industry.
3
However, with regard to aggregate demand, the empirical results are mixed. Some
results suggest that price-cost margins are pro-cyclical, while other results suggest
counter-cyclicality. Various specifications are used when running each model, including
ordinary least squares, fixed effects, and random effects.
This thesis is organized as follows. In Chapter 2, we discuss the historical
development of the literature, focusing on the roles of industry concentration and demand
fluctuations as determinants of profit margins. Next, Chapter 3 presents our two
empirical models, which differ primarily in the number of variables. Specifically, the
second model incorporates several interactive terms to address the influence of changes
in demand on price-cost margins. Chapter 4 discusses the results of the estimation of
linear and entity fixed and random effects versions of the two models. Finally, the thesis
concludes in Chapter 5 with a summary of results and suggestions for future research.
4
Chapter 2
LITERATURE REVIEW
Studies of the determinants of the price-cost margin, which is defined as the
percentage markup of price over marginal cost, can broadly be classified into two
categories. In the first category, studies estimate relatively modest specifications that
emphasize the industry concentration ratio, most commonly taken as the percentage of
the market held by the four largest firms (i.e., the four-firm concentration ratio), as the
key determinant of the price-cost margin. When examining this relationship, a positive
correlation between concentration and the price-cost margin is most commonly estimated,
for which two stories have been provided in support. According to the Differential
Collusion Hypothesis, higher industry concentration increases the likelihood of collusion
in industries, thereby increasing the markup of price over marginal cost (Schmalensee
(1987)). Alternatively, following Demsetz (1973), the Differential Efficiency Hypothesis
states that more efficient firms (i.e., those with lower costs) drive less efficient firms out
of the market, thus increasing concentration and industry profit (i.e., the price-cost
margin).
In the second category, studies estimate more extensive specifications, with the
emphasis of late being on the impact of demand fluctuations on the price-cost margin.4
The typical result from these latter studies is that the price-cost margin is pro-cyclical (i.e.,
4
An early game-theoretic model is that of Green and Porter (1984). Utilizing a trigger
price mechanism within the framework of a cartel, they argue that when prices are falling
(due to falling demand) firms infer cheating has occurred, which induces the cartel to
enter a punishment phase by lowering price (this leads to a pro-cyclical price-cost
margin).
5
the price-cost margin is higher during periods of high demand), which is consistent with
the Green and Porter (1984) story. An alternate hypothesis by Rotemberg and Saloner
(1986), which supports some of the findings in this thesis, argues it is more difficult for
firms to collude when demand is high. Specifically, when demand is high firms have a
greater incentive to cheat on a collusive agreement, since the firm can lower its price
slightly and thereby capture a larger market share until cartel members follow suit during
a “punishment phase”. This implies price-cost margins may be counter-cyclical. Rather
than focus on the game-theoretic literature, though, since the emphasis of this thesis is on
the estimation of an empirical model, we review the empirical literature on price-cost
margins chronologically, with an emphasis on those studies that examine the demand
determinants of the price-cost margin.
In one of the earliest studies, Collins and Preston (1969) examined the
relationship between industry price-cost margins and concentration utilizing data from
U.S. manufacturing industries between 1958 and 1963. The empirical specification was
somewhat simple, as they merely regressed the price-cost margin on the four-firm
concentration ratio and the capital-output ratio. Their principal finding was that industry
concentration had a significantly positive impact on intra-industry price-cost margins,
everything else being equal. Furthermore, they found that industry concentration was
especially significant in explaining the margins of the four largest firms (Collins and
Preston(1969)).
Sawhney and Sawhney (1973) extended the work of Collins and Preston (1969)
by addressing price-cost margins in twenty five Indian manufacturing industries.
6
Averaging observations over a six year period, the price-cost margin was not only
regressed on concentration and the capital-output ratio, but the authors also extended the
basic model by accounting for the degree of capacity utilization, which was calculated as
output as a percent of installed capacity. Sawhney and Sawhney (1973) argued that
higher (lower) rates of capacity utilization reflect higher (lower) demand. Not only did
they find positive impacts of concentration and the capital-output ratio on the price-cost
margin, but they also found margins to be higher in industries with greater capacity
utilization. Accordingly, this early study was one of the first to suggest that price-cost
margins might be pro-cyclical.
McFetridge (1973) used a cross-sectional data set to analyze price-cost margins
for forty-three Canadian manufacturing industries averaged over the 1965-1969 period.
In this study, determinants of the price-cost margin included concentration and the
growth for demand, which he defined as the rate of change of value added sales.
McFetridge’s main finding was that, after controlling for fluctuations in demand and
capital intensity, concentration continued to have a positive impact on the price-cost
margin. However, in testing the coefficient of growth of demand, McFetridge did not
find a significant relationship with industry margins.
Qualls (1979) studied seventy-nine U.S. industries over the time period of 19581970. In this study, the price-cost margin was regressed on industry concentration, two
dummy variables denoting different levels for barriers-to-entry, and several other
independent variables. The dependent variable was calculated by dividing the difference
7
between value-added sales and wages by the value of shipments.5 Several models were
estimated, with the main finding being a consistent positive relationship between the
price-cost margin and concentration. Furthermore, Qualls’ results suggested a significant
positive relationship between concentration and the cyclical variability of price-cost
margins (Qualls (1979)). In other words, during recessions, there was a narrowing of
price-cost margins in highly concentrated industries; during expansions, margins were
more variable in highly concentrated industries.
Neumann, Böbel, and Haid (1983) explored price-cost margins in West German
industries. Variables were constructed from data taken from 283 firms over the 19651977 period. Their empirical model included the usual determinants of the price-cost
margin, such as concentration and growth of sales. Estimating their model on a year-byyear basis, the authors encountered a number of problems, chief of which was the lack of
data for certain years. Nonetheless, the main finding of their research was that industry
price-cost margins were pro-cyclical, increasing during economic expansions and
decreasing during economic downturns. Also, they found in highly concentrated
industries that the effect of demand fluctuations was more pronounced.
These earlier studies principally relied on cross-sectional data to analyze pricecost margins. The paper by Domowitz, Hubbard, and Petersen (1986) was one of the first
5
Value added is defined as revenue minus outside purchases of materials and services.
With regard to the price-cost margin (PCM), since marginal cost (MC) is not easily
observable, PCM is proxied by taking a short cut. Essentially, PCM = (P-MC)/P, where
P is price. Now, if we assume marginal cost equals average cost (AC), then PCM
becomes (P-AC)/P. Multiplying by Q/Q yields: PCM = (P-AC)*Q/P*Q = (P*Q –
AC*Q)/P*Q = (value of shipments – total costs)/value of shipments; or approximately
(value of shipments – material costs – labor costs)/value of shipments, which is
essentially (total value added – wages)/value of shipments.
8
to rely on panel data methods. Specifically, the authors examined price-cost margins for
284 manufacturing industries between the years 1958 and 1981, utilizing a number of
regressors, including concentration, the capital-output ratio, output demand growth
(which accounts for fluctuations in market demand), and the unemployment rate (which
accounts for fluctuations in aggregate demand).
By interacting concentration with the
other independent variables, the key findings of their research were that (i) increases in
demand increased price-cost margins and (ii) this effect was greater in more concentrated
industries.
A short note by Kwoka (1990) continued the research on the effects of demand
fluctuations on price-cost margins. Determinants of the price-cost margin included
concentration, demand growth, and capital intensity. His findings revealed that
concentration had a positive impact on the price-cost margin, but also demand growth
played a key role. Kwoka explained that during periods of economic contraction, the
effect of concentration on price-cost margins practically vanished. Alternatively, the
effect of concentration on margins was enhanced during times of economic growth.
Prince and Thurik (1992) studied sixty-six Dutch industries from 1974 to 1986.
Following a similar path as Domowitz, Hubbard, and Petersen (1986), Prince and Thurik
regressed the price-cost margin on a number of variables, including capacity utilization
and sales growth, which were used as proxies for demand fluctuations. Interacting
concentration with capacity utilization and sales growth, the main intent of their study
was to assess whether the pro-cyclical nature of price-cost margins was more pronounced
in greater or lesser concentrated industries. Similar to Domowitz, Hubbard, and Petersen
9
(1986), they did find that demand fluctuations affected price-cost margins, and that
industry-specific business cycle fluctuations were more important than aggregate
business cycle fluctuations.
Continuing, Mueller and Sial (1993) presented a study which focused on the
cyclicality of price-cost margins, based on unique data collected from the Federal Trade
Commission. Similar to other studies, they treated the price-cost margin as a function of
concentration, sales growth, capacity utilization, and unemployment. In their case,
though, they estimated a series of cross-sectional regressions (i.e., one for each year), as
well as panel regressions, over the 1947-1990 period. Utilizing this alternative data, their
main finding was that the positive relationship between concentration and the price-cost
margin did not manifest itself for most of the years of their data. However, it was
hypothesized by Mueller and Sial that data for the years 1974-76 seriously distorted the
long-run positive relationship between profits and concentration. The authors’ results
illustrated, as had several other studies, the importance of including variables that
captured the effects of cyclical conditions.
The article by Haskel, Martin, and Small (1995) examined price-cost margins in
sixteen UK manufacturing industries over the years 1969 to 1989. They used the
approach employed by Domowitz, Hubbard, and Petersen (1986), as well as other authors,
to estimate a series of industry margin regressions with the intent of examining the role of
the business cycle. Accordingly, determinants of industry margins included various
cyclical variables, such as capacity utilization and unemployment. Their two key
10
findings were that (i) industry margins were pro-cyclical and (ii) industry margins were
higher in more concentrated industries.
The study by Go, Kamerschen, and Delorme (1999) examined the relationship
between the price-cost margin and concentration in the Philippines. The explanatory
variables included in the model were similar to those used in past studies, whereas the
data was for the single year of 1986 and included forty-six manufacturing industries.
Regressing the price-cost margin on concentration, the capital-output ratio, and demand
growth, the positive coefficients on concentration and the capital-output ratio were as
expected. Also, they found price-cost margins were pro-cyclical. Based on their results,
these authors submitted that Philippine manufacturing industries operated in more
monopolistic markets, which reinforced findings from previous studies on Philippine
manufacturing industries.
Culha and Yalçin (2005) analyzed the determinants of price-cost margins in
Turkish manufacturing industries utilizing panel data constructed from four thousand
firm balance sheets over the 1995-2003 period. Price-cost margins were constructed as
the ratio of pre-tax profit to net sales.6 Key regressors included concentration and
demand fluctuations; the regression results supported pro-cyclical price-cost margins in
Turkey.
The research by Dickson (2005) used fixed effects regressions to disentangle
efficiency and market power effects on price-cost margins (i.e., addressing the
differential efficiency and differential collusion hypotheses.). The data obtained from the
6
That is, in their case PCM = (value of shipments-total costs)/value of shipments, which
is (TR – TC)/TR, or profit/sales revenue.
11
U.S. Census Bureau for this study included 253 U.S. manufacturing industries from
1963-1992.7 The price-cost margin was constructed as the difference between price and
average variable cost, all divided by price. Interpolating data on the four-firm
concentration ratio for non-census years, the author determined that a market power
effect existed in that higher industry concentration led to higher price-cost margins. Also,
he found the efficiency effect dominated the collusion effect by revealing that higher
concentration increased industry margins, not by raising price, but by lowering the cost to
produce.
In reviewing the literature, researchers have a myriad of examples to construct the
empirical model needed to examine the determinants of industry price-cost margins. If
looking at U.S. manufacturing industries, one has access to multiple years of data from
the U.S. Census, which makes it fairly simple to construct data for price-cost margins, as
well as a limited set of explanatory variables. Accordingly, the goal of this research is to
look at the most recent data available from the U.S. Census Bureau and to study the
relationship between price-cost margins, industry concentration, capital-output ratios, and
measures of demand volatility.
7
Although at this point Dickson had access to more recent data corresponding to the
North American Industry Classification System (NAICS), he chose not to use NAICS
data.
12
Chapter 3
EMPIRICAL MODEL AND DATA
This chapter discusses the empirical models and the data employed for their
estimation. Emphasis is placed on describing the procedures followed to collect and
analyze this data.
3.1.
Empirical Model
In light of the studies reviewed in the last chapter, which have examined the
influence of concentration, the capital-output ratio (or capital intensity), and demand
fluctuations on price-cost margins, several specifications will be estimated in this thesis.
To begin, the following baseline model (labeled Model 1) is considered:
𝑃𝐶𝑀𝑖𝑡 = 𝛽𝑜 + 𝛽1 𝐶𝑖𝑡 + 𝛽2 (𝐶𝐼)𝑖𝑡 + 𝛽3 𝐺𝑅𝑖𝑡 + 𝛽4 𝑈𝑡 + 𝜀𝑖𝑡
(1)
where PCMit is the price-cost margin in industry i in year t, Cit is concentration in
industry i in year t, and CIit represents capital intensity in industry i in year t. Two
measures of demand fluctuations are also considered as regressors in equation (1). First,
fluctuations in industry-level demand are accounted by GR it , which captures the growth
in demand from one year to the next, defined as the percentage change in the value of
shipments in industry i from year t-1 to year t. Second, aggregate demand fluctuations
are accounted by the unemployment rate in year t (Ut), which does not vary across
industries. The error term is given by εit.
13
In light of our use of panel data, various versions of equation (1) are estimated.
Specifically, in addition to not controlling for panel effects (i.e., estimating equation (1)
with ordinary least squares (OLS), we also consider fixed and random effects versions of
equation (1). For the fixed effects version, the intercept in equation (1) is adjusted to
allow industry-specific intercepts. For the random effects version, the error term is
adjusted to account for additional unexplained industry-specific and year-specific
variation, utilizing a generalized least-squares (GLS) estimation approach.
Next, in light of Domowitz, Hubbard, and Petersen (1986), equation (1) is
modified to allow for interaction terms. Specifically, we also consider the amended
version of Model 1 (labeled Model 2):
𝑃𝐶𝑀𝑖𝑡 = 𝛽𝑜 + ⋯ + 𝛽5 𝐶𝑖𝑡 𝐺𝑅𝑖𝑡 + 𝛽6 𝐶𝑖𝑡 𝑈𝑡 + 𝛽7 𝐶𝐼𝑖𝑡 𝐺𝑅𝑖𝑡 + 𝛽8 𝐶𝐼𝑖𝑡 𝑈𝑡 + 𝜀𝑖𝑡
(2)
In order to address the influence of changes in demand on price-cost margins, we add
regressors incorporating interactive terms. The growth of sales variable (GRit) acts as an
indicator of industry-specific demand fluctuations, while annual unemployment (Ut) is an
“aggregate” indicator of demand. In equation (2), these two variables are combined with
concentration and capital intensity to explore the effects that demand changes have on the
impacts of concentration and capital intensity on price-cost margins.8
8
𝛿𝑃𝐶𝑀
𝛿𝑃𝐶𝑀
That is, from equation (2) we get: 𝛿𝐶 = 𝛽1 + 𝛽5 𝐺𝑅𝑖𝑡 + 𝛽6 𝑈𝑡 and 𝛿𝐶𝐼 = 𝛽2 +
𝛽7 𝐺𝑅𝑖𝑡 + 𝛽8 𝑈𝑡 . Hence, our two demand fluctuation variables affect the influence of
concentration and capital intensity on the price-cost margin.
14
Similar to the approach used for equation (1), various versions of equation (2) are
estimated. Specifically, in addition to ordinary least squares, we also consider entityfixed and random effects versions of equation (2). For the fixed effects version, the
intercept in equation (2) is adjusted to allow industry-specific intercepts. For the random
effects version, the error term is adjusted to account for additional unexplained industryspecific and year-specific variation, utilizing a generalized least-squares (GLS)
estimation approach.
Different versions of equations (1) and (2) are estimated. Specifically, we
separately account for three different measures of concentration, namely the four-firm
concentration ratio, the eight-firm concentration ratio, and the Herfindahl index, to see
whether or not the impact of concentration on the price-cost margin is sensitive to the
measure of concentration. Furthermore, equations (1) and (2) are estimated with different
data, corresponding to industries defined either by the Standard Industry Classification
(SIC) system or the North American Industry Classification System (NAICS) system, to
see the extent to which the results are sensitive to differences in data.
3.2.
SIC Data
Data at the four-digit SIC industry-level were obtained from the Annual Survey of
Manufactures and the Census of Manufactures via the website of the U.S. Census Bureau
(http://www.census.gov/). Concerning the variables in equations (1) and (2), yearly data
from the Annual Survey of Manufactures were used to construct price-cost margins,
𝑉𝑎𝑙𝑢𝑒 𝐴𝑑𝑑𝑒𝑑−𝑃𝑎𝑦𝑟𝑜𝑙𝑙
defined as 𝑉𝑎𝑙𝑢𝑒 𝐴𝑑𝑑𝑒𝑑−𝐶𝑜𝑠𝑡 𝑜𝑓𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙𝑠 , where value added is the difference between the
15
value of shipments and the cost of materials, and payroll refers to the cost of labor. Also
collected from the Annual Survey of Manufacturers, capital intensity is defined as
𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠
𝑇𝑜𝑡𝑎𝑙 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠
, while fluctuations in industry-level demand are measured as
𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠𝑡 − 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠𝑡−1
𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠𝑡−1
.
As for the concentration measures, the Census of Manufacturers provides data
every five years (beginning in 1967) on the four-firm concentration ratio (i.e., the percent
of market output produced by the four largest firms), the eight-firm concentration ratio
(i.e., the percent of market output produced by the eight largest firms), and the Herfindahl
index (approximated by the squared sum of the market shares of the largest 50 firms).
Accordingly, given this data is only available every five years, equations (1) and (2) are
initially estimated using data for the years 1967, 1972, 1977, 1982, 1987, and 1992 for
459 industries. As discussed later, though, we used Stata (version 10.1) to interpolate
annual missing observations for concentration, and then re-estimated the three
specifications with this more extensive data. Table 3.1 defines the relevant variables for
our models.
Table 3.1 Variable Definitions
Variable
Definition
PCM
Price-cost margins or profit margins
C4
Four-firm concentration ratio
C8
Eight-firm concentration ratio
H50
Herfindahl index (constructed from largest 50 firms)
CI
Capital intensity
GR
Annual percentage change in industry sales
U
Annual unemployment rate
16
Descriptive statistics for the SIC data are provided in Table 3.2. As the table
indicates, there are substantial differences in the number of observations across the
variables. The total number of observations for the concentration data set ranges from
1,244 to 2,255. For the four-firm and eight-firm concentration variables, the number of
observations in 1967 is 279. For 1977 and 1982, the number of observations is around
350. After 1987, the number of observations for industry concentration goes up to
around 450. For the Herfindahl index, there are no observations available before 1982.
In 1982, there are approximately 350 observations, and after 1987 there are
approximately 450 observations each year. The different numbers of observations in a
given year for the various measures of concentration is likely due to changes in the data
collection methods by the Census. After constructing the price-cost margins, capital
intensity, and growth of sales, 11,934 total observations are acquired for each variable.
The unemployment rate is identical across industries and so it only varies over time.
Table 3.2 Descriptive Statistics (SIC)
Variable
Obs.
Mean Std. Dev.
PCM
11934
0.2805
0.0882
C4
2255
0.3903
0.2077
C8
2249
0.5209
0.2309
H50
1244
0.2323
0.2115
CI
11934
0.7214
0.5298
GR
11934
0.0711
0.1355
U
11934
0.0637
0.0159
Min
-0.0606
0.0100
0.0200
0.0003
0.0570
-0.7686
0.0350
Max
0.7927
0.9900
1.0000
0.9997
8.7973
3.1333
0.0970
In Table 3.3, we present a correlation matrix for the seven variables. There is
relatively strong correlation between price-cost margins and the three different measures
of concentration. Also, to be expected, there is very high positive correlation between all
17
three concentration measures. Indeed, it is most pronounced between the four-firm and
eight-firm concentration ratios, as well as between the Herfindahl index and the four-firm
concentration ratio. This strong relationship would present an issue of multicollinearity if
more than one concentration ratio was used in the same equation, and it is partly for this
reason that we do not specify such a model.
Table 3.3 Correlation Matrix (SIC)
PCM
C4
C8
PCM
1
C4
0.1759 1
C8
0.1422 0.9754 1
H50
0.195
0.9434 0.8851
CI
-0.0773 0.1299 0.1677
GR
0.1233 -0.1106 -0.1326
U
-0.1589 0.0002 0.0075
H50
CI
GR
1
0.1147 1
-0.1058 -0.3629 1
-0.0039 0.168
-0.3104
U
1
Continuing our description of the data, Table 3.4 presents mean price-cost
margins for different quintiles of the four-firm concentration ratio in each of three
different time periods. As indicated, for each time period the price-cost margin steadily
increases as industries become more concentrated. Also, we can see price-cost margins
are steadily increasing over time, although for the two highest quintiles there is a slight
dip in price-cost margins from the 1967-73 to the 1974-81 periods.
Table 3.4 Price-Cost Margins by Concentration Quintile (SIC)
Period
0≤C4≤.2
.21≤C4≤.4
.41≤C4≤.6
.61≤C4≤.8
1967-1973
0.244
0.261
0.27
0.302
1974-1981
0.252
0.27
0.271
0.299
1982-1992
0.272
0.29
0.299
0.328
.81≤C4≤1.0
0.34
0.309
0.375
18
Figures 3.1-3.4 also illustrate changes in key variables over time. In particular,
Figure 3.1 shows the mean value of the price-cost margin for all industries over time,
which indicates a steadily increasing value over time. Indeed, Figure 3.1 shows the
increase in the average price-cost margin has been most pronounced from the 1980s
onward. This is also supported in Table 3.4, as the greatest increase in the price-cost
margin occurs between the 1974-81 and 1982-92 periods.
.26
.27
.28
.29
.3
.31
Figure 3.1 Average Price-Cost Margins
1965
1970
1975
1980
Year
1985
1990
Figures 3.2-3.4 further illustrate the variation of average price-cost margins over
time. In these figures, different ranges of price-cost margins are used to see if the trend
of average industry margins is similar. The different ranges of average price-cost
margins are divided into low, middle and high ranges. The low range is for price-cost
margins less than .24; the middle range is for margins greater than .24 and less than .31;
finally, the high range is for margins greater than .31. The ranges were chosen in a way
as to evenly distribute the 11,934 observation across the three strata.
19
.175
.18
.185
.19
.195
.2
Figure 3.2 Low-Range Price-Cost Margins
1965
1970
1975
1980
Year
1985
1990
.27
.272
.274
.276
.278
.28
Figure 3.3 Mid-Range Price-Cost Margins
1965
1970
1975
1980
Year
1985
1990
20
.36
.37
.38
.39
.4
Figure 3.4 High-Range Price-Cost Margins
1965
1970
1975
1980
Year
1985
1990
As Figures 3.2-3.4 indicate, industries with low and high price-cost margins saw
average price-cost margins decline somewhat up through the 1970s, and since the early
1980s price-cost margins have increased in these industries; whereas mid-range industry
margin have seen increasing price-cost margins over this period, but with noticeable
volatility in these margins as well. Accordingly, finding there are differences in the
trends of price-cost margins across industry groups suggests a need to address panel data
issues in the estimation of our model.
Finally, Figure 3.5 illustrates trends in the average price-cost margin and fourfirm concentration ratio over the 1967-92 period, while Figure 3.6 illustrates trends in the
average price-cost margin, the unemployment rate, and the average growth rate of sales
21
values. As illustrated in Figure 3.5, there is a noticeable positive correlation between
concentration and the price-cost margin, particularly since the mid-1980s.
.25
.3
.35
.4
Figure 3.5 Industry Concentration and Price-Cost Margins
1965
1970
1975
1980
Year
Average PCM
1985
1990
Average C4
Inspecting Figure 3.6, however, it is difficult to identify a relationship between
demand fluctuations, as proxied by the unemployment rate and the growth in industry
sales, and price-cost margins. Accordingly, we leave it to the regression results in the
next chapter to draw any conclusions on the relationship between the business cycle and
price-cost margins.
22
0
.1
.2
.3
Figure 3.6 Cyclical Behavior of Price-Cost Margins
1965
1970
1975
1980
Year
Average PCM
Unemployment
3.3.
1985
1990
Average GR
NAICS Data
Although the bulk of the estimation of equations (1) and (2) will be done using
SIC data, we will extend some of the results (as discussed in the next chapter) using data
from the NAICS system. Briefly, the NAICS system was adopted in 1997 to facilitate
industry classification across the U.S., Mexico, and Canada, following the adoption of the
North American Free Trade Agreement (NAFTA). Since 6-digit NAICS industries are
most easily comparable to 4-digit SIC industries, we obtained from the U.S. Census
Bureau website 6-digit NAICS data on our variables from the Annual Survey of
23
Manufacturers and the Census of Manufacturers for 473 industries over the 1997-2002
period.9
Table 3.5 provides descriptive statistics for our variables constructed using the
NAICS data.10 Like the SIC data, price-cost margins, capital intensity, and growth of
sales are available annually with approximately 470 observations each year. Four-firm
concentration data is available in 1997 and 2002, and the number of observations is 471
each year. Like before, the annual unemployment rate is extrapolated to every industry in
a given year.
Table 3.5 Descriptive Statistics (NAICS)
Variable
Obs.
Mean Std. Dev.
Min
PCM
2838
0.3255
0.0995 0.0650
C4
942
0.4229
0.2122 0.0200
CI
2838
0.5109
0.3507 0.0710
GR
2827
0.0099
0.1645 -0.7166
U
2838
0.0468
0.0058 0.0400
Max
0.8910
1.0000
4.2660
2.9389
0.0580
In Table 3.6, we present the correlation matrix for the NAICS data. Again, there
is a fairly high positive correlation between the price-cost margin and four-firm industry
concentration. In fact, the level of correlation is nearly identical to the level found in the
9
Initially, we considered using NAICS data beyond 2002. However, changes in the
definition of capital expenditure significantly affected values of capital intensity beyond
2002. Accordingly, in order to maintain consistency in our data, we chose to limit our
use of NAICS data to the 1997-2002 period; and so, since the Census of Manufacturers
only provides observations on concentration every five years, for the estimation of
equations (1) and (2) we only have data for two years, 1997 and 2002.
10
As explained in the next chapter, when using NAICS data we chose to rely on the fourfirm concentration ratio as our measure of concentration. Therefore, Table 3.5 does not
report descriptive statistics for the eight-firm concentration ratio and Herfindahl index.
24
SIC data (.182 compared to .176). There is also strong correlation of capital intensity
with industry concentration, growth of sales, and unemployment. These correlations may
be influenced by limitations found in the data (i.e., the limited number of years being
considered).
Table 3.6 Correlation Matrix (NAICS)
PCM
C4
CI
GR
PCM
1
C4
0.182
1
CI
0.0879
0.208
1
GR
0.0451 -0.0005 -0.2453
1
U
0.0299 0.0533 0.2388 -0.1407
U
1
25
Chapter 4
ESTIMATION RESULTS
This chapter presents estimation results from the various model specifications
presented in the last chapter. Before discussing the results, it is useful to review the
estimation methods and discuss some estimation issues.
In the last chapter, we described the first specification (Model 1) as our baseline
model which is similar to past studies which regressed the price-cost margin on a
measure of concentration, capital expenditures, and measures of sales and unemployment.
Model 2 presents a more extensive specification by exploring in greater detail the impact
of demand fluctuations on profit margins. Since we are using panel data for the
estimation of each model, we employ industry fixed effects to allow for industry-specific
intercepts. We also employ a random effects estimation to account for additional
unexplained industry-specific and year-specific variation.
In order to explore how different measures of concentration affect price-cost
margins, initially in separate regressions we use the four-firm and eight-firm
concentration ratios, as well as the Herfindahl index (constructed using the market shares
of the largest 50 firms in each industry), as measures of concentration. Also, three
different sets of data are used in this study. Specifically, for the majority of regressions
we use the four-digit SIC coding system over 5-year intervals to differentiate among
various manufacturing industries. Next, we interpolate annual observations for our 4digit SIC industries in an effort to increase sample size. For the last set of estimates, we
26
use the six-digit NAICS method for identifying various industries. For simplicity, in the
latter regressions we simply use the four-firm concentration ratio as the measure of
concentration.
4.1
Table 4.1 Estimation Results
Since the four-firm concentration (C4) is the most often used measure of
concentration in the literature, we begin in Table 4.1 by presenting the results based on its
use as our measure of concentration. Specifically, in the first three columns of Table 4.1
we present the results for Model 1, with the column 1 results corresponding to no panel
treatments (i.e., OLS), followed by the industry fixed effects (FE) and random effects
(RE) in columns 2 and 3. In columns 4-6 the Model 2 results are presented, with column
4 corresponding to no panel treatment, while columns 5 and 6 provide the FE and RE
results, respectively.
Observations
R-squared
F test
Chi-square test
Number of sic
Constant
CI*U
C4*U
CI*GR
C4*GR
Unemployment (U)
Growth of Sales (GR)
Capital Intensity (CI)
Concentration (C4)
2,255
0.053
21.57
0.22868***
(0.010)
0.10167***
(0.012)
0.00071
(0.004)
0.03778***
(0.013)
0.18887
(0.121)
2,255
0.24542***
(0.008)
0.13273***
(0.012)
-0.01745***
(0.002)
0.02508***
(0.006)
0.01978
(0.059)
2,255
0.060
13.75
0.14372***
(0.043)
0.03762***
(0.012)
0.02464
(0.027)
0.91725***
(0.247)
0.00526
(0.093)
0.01354
(0.019)
-0.61526
(0.629)
-0.62907***
(0.200)
0.18177***
(0.017)
(4) OLS
PCM
220.2
459
459
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
2,255
0.102
50.96
0.23809***
(0.008)
0.14777***
(0.015)
-0.01868***
(0.002)
0.02440***
(0.006)
0.00723
(0.059)
Table 4.1 Results Incorporating Four-Firm Concentration (SIC)
(1) OLS
(2) FE
(3) RE
VARIABLES
PCM
PCM
PCM
459
2,255
0.160
42.72
0.16305***
(0.022)
0.03021***
(0.005)
0.05632***
(0.012)
0.79752***
(0.121)
-0.01989
(0.029)
-0.03970***
(0.007)
-0.37035
(0.248)
-1.05299***
(0.100)
0.20083***
(0.010)
(5) FE
PCM
347.2
459
2,255
0.15680***
(0.021)
0.03030***
(0.005)
0.05342***
(0.012)
0.79932***
(0.121)
-0.01821
(0.029)
-0.03518***
(0.007)
-0.38754
(0.249)
-1.00053***
(0.099)
0.20390***
(0.010)
(6) RE
PCM
27
28
The ordinary least squares (OLS) results from the first column in Table 4.1 are
consistent with the literature in that four-firm industry concentration has a significantly
positive impact on the price-cost margin. Furthermore, the growth of sales also has a
positive and significant impact. As such, it appears price-cost margins are pro-cyclical
with respect to industry demand. However, the R-squared is very low, which implies
there is much unexplained variation in price-cost margins. Also, with respect to the OLS
results, capital intensity and the unemployment rate do not significantly affect the pricecost margin.
In column 2, when adding industry fixed effects, the independent variables gain
explanatory power, as evidenced by (i) R-square doubling in size and (ii) finding that the
coefficient of capital intensity is negative and significantly different from zero.
Nonetheless, since capital intensity is typically taken to be a measure of entry barriers
(i.e., higher capital intensity signifies a greater entry barrier), we would expect its
coefficient to be positive, not negative.11 The random effects results in column 3 are
similar to the fixed effects results (i.e., the coefficients are similar in sign, magnitude, and
significance). Lastly, at the bottom of Table 1 a series of test statistics are provided to
evaluate Model 1. Specifically, F-tests of overall fit for the OLS and fixed effects
regressions support the joint significance of the regression coefficients. Also, a Hausman
chi-square test of the fixed effects versus the random effects results favors the fixed
effects specification.
11
Given that capital intensity is calculated as
𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠
𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠
, a higher ratio for a
given industry suggests that a new competitor needs to invest a higher amount of capital
in order to enter the industry, thus deterring entry.
29
Columns 4-6 in Table 4.1 employ various interactive terms to assess whether or
not demand fluctuations affect the impacts of concentration and capital intensity on the
price-cost margin. Comparing the R-square from columns 4 and 5 to the counterparts in
columns 1 and 2, respectively, the additional regressors in columns 4 and 5 slightly
improve the overall fit of the model. Also, as expected F-tests favor the joint
significance of the coefficients in the OLS and FE regressions, while the Hausman chisquare statistic continues to favor fixed effect over random effects.
Turning to the individual coefficients, we find for the OLS results in column 4
that the coefficients of the four-firm concentration ratio, capital intensity, and the
unemployment rate are positive and statistically significant. Accordingly, as expected
not only do higher concentrated industries with higher levels of capital intensity tend to
have higher price-cost margins, but unlike the Model 1 results the price-cost margins
appears to now be counter-cyclical at the aggregate level, ceteris paribus.12 Furthermore,
the coefficients of the interactive terms are generally not statistically significant, with the
exception of the interaction between capital intensity and the unemployment rate. Indeed,
the coefficient of this interaction term being significantly negative means (i) the positive
impact of capital intensity on the price-cost margin is much lower during periods of high
unemployment and (ii) the counter-cyclical nature of price-cost margins is dampened in
industries with high capital intensity.
12
This hypothesis is supported by the findings of Rotemberg and Saloner (1986) who
examined collusion by way of a game-theory model. They found the incentive to cheat
on a cartel agreement is greatest during periods of high demand, which forces the cartel
to price more competitively during such periods to reduce the temptation to cheat.
30
In column 5, fixed effects are used in the estimation of Model 2. Not only is there
an improvement in overall fit, as the R-square increases to 0.16, but now we find the
coefficients of concentration, capital intensity, sales growth, and unemployment are all
positive and statistically significant (and of similar magnitude to the OLS results). As
such, now we find price-cost margins are pro-cyclical with respect to industry demand
but counter-cyclical with respect to aggregate demand, ceteris paribus. Furthermore, the
coefficients of the interaction terms involving concentration (i.e., C4*GR and C4*U) are
not statistically significant, while the coefficients of the interaction terms involving
capital intensity (i.e., CI*GR and CI*U) are negative and statistically significant. This
indicates (i) that the impact of concentration on price-cost margins is largely insensitive
to demand fluctuations, (ii) the positive impact of capital intensity on price-cost margins
is dampened during periods of high industry demand, and (iii) the impact of demand
fluctuations on price-cost margins is lower in those industries with higher capital
intensity. The random effects results in column 6 reveal coefficients that are similar in
sign, magnitude, and significance to the fixed effects results, although the Hausman test
favors the fixed effects over the random effects specification.
As evidenced by the results on Table 4.1, therefore, there are general trends in the
signs of the coefficients and their statistical significance. In particular, across all models
the level of industry concentration is positive and statistically significant, while the
coefficient of the industry growth variable is positive and generally significant. However,
when interaction terms are included, we do find the roles of demand fluctuations and
capital intensity are inter-connected.
Observations
R-squared
F test
Chi-square test
Number of sic
Constant
CI*U
C8*U
CI*GR
C8*GR
Unemployment (U)
Growth of Sales (GR)
Capital Intensity (CI)
Concentration (C8)
2,249
0.037
17.90
0.23092***
(0.011)
0.07227***
(0.009)
0.00095
(0.004)
0.04210***
(0.012)
0.16390
(0.121)
2,249
0.23862***
(0.009)
0.10830***
(0.011)
-0.01679***
(0.002)
0.03510***
(0.006)
0.02334
(0.059)
2,249
0.046
11.94
0.12644***
(0.035)
0.03744***
(0.012)
-0.00222
(0.026)
1.08952***
(0.262)
0.06851
(0.050)
0.00523
(0.017)
-0.87285*
(0.517)
-0.62598***
(0.205)
0.17399***
(0.018)
(4) OLS
PCM
201.5
459
459
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
2,249
0.100
49.56
0.22366***
(0.010)
0.13301***
(0.015)
-0.01787***
(0.002)
0.03492***
(0.006)
0.01291
(0.060)
Table 4.2 Results Incorporating Eight-Firm Concentration (SIC)
(1) OLS
(2) FE
(3) RE
VARIABLES
PCM
PCM
PCM
459
2,249
0.166
44.27
0.14582***
(0.021)
0.02997***
(0.006)
0.03681***
(0.014)
0.85632***
(0.134)
0.04650**
(0.022)
-0.04938***
(0.007)
-0.43098*
(0.224)
-1.05367***
(0.100)
0.18879***
(0.012)
(5) FE
PCM
346.7
459
2,249
0.13345***
(0.019)
0.03004***
(0.006)
0.03348**
(0.014)
0.86955***
(0.135)
0.04813**
(0.022)
-0.04497***
(0.007)
-0.46792**
(0.224)
-1.00114***
(0.099)
0.19627***
(0.012)
(6) RE
PCM
31
32
4.2
Table 4.2 Estimation Results
Table 4.2 presents regression results of Models 1 and 2 using the eight-firm
concentration ratio (C8) as the measure of industry concentration. To begin, as
evidenced by the R-square values from columns 1-6 in Table 4.2, we can see that the
different models explain slightly less variation in the SIC price-cost margin data when
using the eight-firm concentration measure, compared to the four-firm measure.
Nonetheless, the F test and Chi-squared tests continue to reject their respective null
hypotheses, implying (i) the models are overall significant and (ii) fixed effects are
preferred to random effects.
Turning to Model 1, the results in columns 1-3 are similar to their counterparts in
Table 4.1. Specifically, the coefficient of C8 is positive and statistically significant, the
coefficient of CI is negative and statistically significant in the two regressions controlling
for panel effects, and the coefficient of GR is positive and statistically significant. The
unemployment rate in this specification continues to be of little significance in model 1.
Columns 4-6 provide results for the extended model using C8 as the measure of
industry concentration. The results are similar to those given by Table 4.1 in that the
coefficients of C8, CI, GR, and U are generally positive and statistically significant. Also,
in the OLS specification (column 4) the term CI*U indicates that higher unemployment
(a higher level of capital intensity) reduces the positive impact of capital intensity
(unemployment rate) on price-cost margins.
33
Interestingly, estimating Model 2 with fixed effects and random effects changes
the roles of the interaction terms compared to Table 4.1. With respect to columns 5 and 6,
while similar to Table 4.1, we find the coefficients of CI*GR and CI*U are significantly
negative; furthermore, we now find significance in the coefficients of the interaction
terms involving industry concentration (i.e., C8*GR and C8*U). Concerning C8*GR, its
coefficient being positive and significant (at the 5% level) tells us two things. First, the
positive impact of concentration on price-cost margins is greater during periods of high
industry demand growth. Second, the pro-cyclical nature of price-cost margins is more
pronounced in higher concentrated industries. As for the coefficient of C8*U, its
coefficient being negative means (i) the positive impact of concentration on price-cost
margins is lower during periods of high unemployment and (ii) the positive impact of
unemployment on price-cost margins is lower in highly concentrated industries. Taken
together, these results imply, for instance, the cyclical nature of price-cost margins very
much hinges on the degree of concentration in the market.
In conclusion, while there are similarities in the results presented in Tables 4.1
and 4.2 (e.g., the impact of concentration on the price-cost margin is positive in all
regressions, ceteris paribus), there are key differences. In particular, it appears that the
cyclicality of price-cost margins depends on the measure of concentration.
Observations
R-squared
F test
Chi-square test
Number of sic
Constant
CI*U
H50*U
CI*GR
H50*GR
Unemployment (U)
Growth of Sales (GR)
Capital Intensity (CI)
Concentration (H50)
1,244
0.076
18.65
0.34238***
(0.016)
0.09301***
(0.017)
-0.01530*
(0.009)
0.05949***
(0.021)
-0.84450***
(0.193)
1,244
0.36867***
(0.009)
0.06501***
(0.012)
-0.06294***
(0.008)
0.01188
(0.009)
-0.74176***
(0.079)
(3) RE
PCM
(4) OLS
PCM
1,244
0.092
12.41
0.14211
(0.098)
-0.02652
(0.047)
-0.06010
(0.039)
-0.78649**
(0.395)
0.18348
(0.160)
0.15856***
(0.053)
-0.74802
(1.247)
0.23432
(0.576)
0.33955***
(0.032)
303.9
457
457
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
1,244
0.279
75.73
0.38651***
(0.009)
0.03050*
(0.016)
-0.08741***
(0.009)
0.00144
(0.009)
-0.71631***
(0.080)
Table 4.3 Results Incorporating Herfindahl Index (SIC)
(1) OLS
(2) FE
VARIABLES
PCM
PCM
457
1,244
0.294
40.61
0.08408**
(0.033)
-0.04272*
(0.024)
-0.02008
(0.018)
-0.31923*
(0.167)
0.06421
(0.040)
0.01246
(0.025)
-0.73439*
(0.399)
-0.47221*
(0.254)
0.35325***
(0.015)
(5) FE
PCM
330.7
457
1,244
0.10418***
(0.032)
-0.01423
(0.022)
-0.02571
(0.017)
-0.37267**
(0.168)
0.08432**
(0.039)
0.03366
(0.024)
-0.54415
(0.396)
-0.50190**
(0.254)
0.33727***
(0.015)
(6) RE
PCM
34
35
4.3
Table 4.3 Estimation Results
Table 4.3 presents estimation results for Models 1 and 2 using the Herfindahl
index (H50) as the measure of industry concentration. Using the Herfindahl index as a
measure of concentration presents several interesting results with respect to the overall fit
of the model. First, F tests and Chi-squared tests reject the null in each case, supporting
the coefficients being jointly different from zero, as well as favoring fixed effects over
random effects. Also, R-square is higher across all specifications compared to those in
Tables 4.1 and 4.2.
Columns 1-3 show the results for the estimation of Model 1. Across the board,
H50 and GR are positive, with the concentration variable having greater significance in
the OLS and random effects specifications, and the growth of sales coefficient being
significant in the OLS regression only. The coefficients of CI and U are negative and
statistically significant. The significantly negative coefficient of unemployment is
different from the last two tables, where the unemployment coefficient was positive but
insignificant. Thus, it appears the measure of concentration does impact the role of
unemployment, as we now find evidence that price-cost margins are pro-cyclical with
respect to fluctuations in aggregate demand.
As before, columns 4-6 present the results for specifications based on Model 2.
There are some notable differences in these results when comparing them to the
respective counterparts in Tables 4.1 and 4.2. In column 4, for example, the coefficient
of H50 is not statistically significant. Furthermore, the coefficients of CI and GR are also
36
insignificant, while the coefficient of the unemployment rate remains negative and
significant at the 10% level.
Moving to the interactive terms, CI*GR is positive and statistically significant in
the OLS regression. This indicates that the impacts of capital intensity and sales growth
are inter-related. Looking at column 5, the fixed effects results show low significance
(i.e., typically 10%) across the board for the coefficients, with the only coefficient which
is significant beyond the 10% level being H50. Other coefficients which are significant
at the 10% level are those tied to CI, U, H50*U, and CI*U. The random effects
specification does not present notable differences in that the coefficient of H50 is positive
and statistically significant, while the coefficient of H50*GR is positive (and now
significant at the 5% level), and the coefficient of CI*U is negative and significant at the
5% level.
Some general trends can be observed among the various specifications in Table
4.3. The impact of industry concentration is positive and statistically significant, while
CI and U are generally statistically significant when used in the baseline model.
However, when looking at the extended model, these coefficients lose some of their
explanatory power. The interactive terms do not show any notable results aside from a
positive and statistically significant coefficient for CI*GR in the OLS results and CI*U in
the panel effects results.
Observations
R-squared
F test
Chi-square test
Number of sic
Constant
CI*U
C4i*U
CI*GR
C4i*GR
Unemployment (U)
Growth of Sales (GR)
Capital Intensity (CI)
Concentration (C4i)
9,509
0.055
100.8
0.23499***
(0.005)
0.09013***
(0.005)
0.00311
(0.002)
0.07424***
(0.007)
0.06880
(0.062)
9,509
0.24797***
(0.005)
0.13726***
(0.007)
-0.02004***
(0.001)
0.04295***
(0.003)
-0.07323**
(0.029)
9,509
0.059
58.58
0.14146***
(0.023)
0.02823***
(0.007)
0.04890**
(0.019)
0.74050***
(0.139)
0.00922
(0.038)
0.02761
(0.018)
-0.79512**
(0.343)
-0.45685***
(0.119)
0.19420***
(0.009)
(4) OLS
PCM
915.0
459
459
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
9,509
0.091
226.1
0.24159***
(0.004)
0.14458***
(0.008)
-0.02036***
(0.001)
0.04263***
(0.003)
-0.07459***
(0.029)
Table 4.4 Results Incorporating Interpolated Four-Firm Concentration (SIC)
(1) OLS
(2) FE
(3) RE
VARIABLES
PCM
PCM
PCM
459
9,509
0.121
155.4
0.17908***
(0.011)
0.01989***
(0.003)
0.04974***
(0.007)
0.72343***
(0.063)
-0.00121
(0.014)
-0.01392***
(0.004)
-0.60460***
(0.127)
-0.80078***
(0.050)
0.19864***
(0.005)
(5) FE
PCM
1247
459
9,509
0.17362***
(0.011)
0.01993***
(0.003)
0.04977***
(0.007)
0.72230***
(0.063)
-0.00131
(0.014)
-0.01335***
(0.004)
-0.60957***
(0.128)
-0.79210***
(0.050)
0.20414***
(0.006)
(6) RE
PCM
37
38
4.4
Table 4.4 Estimation Results
Table 4.4 presents the estimation results for Models 1 and 2 using an interpolated
variation on SIC based four-firm concentration (labeled C4i) as a measure of industry
concentration. Specifically, since the data on concentration are only available every five
years, whereas data on all other variables are available on an annual basis, we interpolate
the missing observations for the four-firm concentration ratio using STATA (version
10.1). Accordingly, the sample size increases substantially.
For these regressions, which are most comparable to those presented in Table 4.1,
the aim is to investigate the impact of additional observations and thus statistical power
on the results. Inspecting the results in Table 4.4, the expected positive relationship
between industry concentration and the price-cost margin remains intact. Also, similar to
Table 4.1 the coefficients of CI and GR are generally negative and positive, respectively,
as well as significantly different from zero at the 1% level. The coefficient of the
unemployment rate is negative and holds some significance in the fixed effects and
random effects models. R-squared values are very close to those generated by the results
from Table 4.1, and F-tests and Hausman tests are similar to those reported in Table 4.1.
Thus, interpolating the missing observations for concentration has modest impacts on the
results for model 1.
As before, columns 4-6 present the results from estimating model 2 with the
interpolated observations of concentration. There are some notable results here.
Specifically, all the coefficients of the independent variables (not including the
39
interaction terms) are positive and statistically significant. Thus, higher levels of industry
concentration, capital expenditures, sales growth, and unemployment lead to higher
price-cost margins. Turning to the interaction terms, across the three specifications the
coefficient of C4i*GR is not significantly different from zero. However, in the fixed
effects and random effects regressions, CI*GR is negative and significant at the 1% level.
Similar to the results in Table 4.1, this indicates the impacts of industry demand
fluctuations and capital intensity on price-cost margins are inter-related. Also, similar to
Table 4.1., we continue to find a negative and statistically significant coefficient
associated with CI*U. Interestingly, though, unlike Table 4.1 the coefficient of C4i*U is
negative and statistically significant in the fixed and random effects regressions. Thus,
similar to the results based on the eight-firm concentration ratio, it appears the influence
of concentration on price-cost margins depends on aggregate economic conditions.
Similarly to the results from Tables 4.1 and 4.2, the regressions incorporating C4i
show some of the expected signs of the various determinants of the price-cost margin.
Primarily, the coefficient of industry concentration is positive and significantly different
from zero, the coefficient of CI is generally significant, the coefficient of GR is positive
and statistically significant, and the coefficient of the unemployment rate is generally
positive and significant (but only in the extended model). Amongst other results, the
interaction terms show that the positive impact of concentration on the price-cost margin
is lower during periods of higher unemployment.
Observations
R-squared
F test
Chi-square test
Number of naics
Constant
CI*U
C4*U
CI*GR
C4*GR
Unemployment (U)
Growth of Sales (GR)
Capital Intensity (CI)
Concentration (C4)
931
0.040
9.798
0.26992***
(0.038)
0.07775***
(0.019)
0.01824**
(0.009)
0.02707*
(0.014)
0.31160
(0.724)
931
0.25326***
(0.017)
0.08527***
(0.017)
-0.00873
(0.009)
0.03674***
(0.008)
0.82412**
(0.324)
931
0.049
7.050
0.09545
(0.220)
0.25724**
(0.113)
0.03179
(0.023)
2.43975*
(1.453)
-0.09078
(0.076)
0.09877***
(0.035)
-0.32226
(4.064)
-4.19864**
(2.036)
0.15145*
(0.078)
(4) OLS
PCM
60.61
473
473
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
931
0.109
13.82
0.24537***
(0.018)
0.07437**
(0.031)
-0.03822***
(0.012)
0.03208***
(0.008)
1.33942***
(0.364)
Table 4.5 Results Incorporating Four-Firm Concentration (NAICS)
(1) OLS
(2) FE
(3) RE
VARIABLES
PCM
PCM
PCM
473
931
0.120
7.672
0.04408
(0.083)
0.04295
(0.085)
0.00577
(0.018)
1.68734**
(0.764)
0.08070**
(0.038)
-0.01846
(0.026)
0.60709
(1.440)
-1.40532
(1.317)
0.22293***
(0.045)
(5) FE
PCM
70.69
473
931
0.06056
(0.079)
0.18014***
(0.068)
0.01087
(0.017)
1.99144***
(0.755)
0.04945
(0.037)
0.02674
(0.022)
0.38675
(1.439)
-3.15028***
(1.135)
0.18340***
(0.042)
(6) RE
PCM
40
41
4.5
Table 4.5 Estimation Results
Table 4.5 presents estimation results for Models 1 and 2 employing the six-digit
NAICS data on manufacturing firms. Since the NAICS is the current system used to
classify industries, this data is more current and thus allows us to examine whether or not
the results based on older data (from Table 4.1-4.4) are robust when using more recent
data. Similar to the last table, though, for the sake of simplicity we also use the four-firm
concentration ratio as our measure of concentration, and thus these results are most
comparable to those presented in Table 4.1.
Using NAICS data does have a limitation, however, in that only two years of data
are available. Nonetheless, as provided in Table 4.5, some of the expected relationships
are still found using this data. Specifically, the four-firm concentration ratio has a
positive and significant effect on the price-cost margin, which is in line with the literature.
In the Model 1 results, the coefficient of CI is positive in the OLS results but negative in
the panel effect results. Also, across all three Model 1 regressions, the coefficient of GR
is positive and statistically significant, indicating that industries experiencing growing
sales will have higher margins. R-square values for the baseline model are modest but
similar in magnitude as those reported in Table 4.1. Furthermore, the F-test and
Hausman test results are also similar to those in Table 4.1.
For the regressions using the extended model (columns 4-6), as expected the Rsquare is higher when compared to the basic regressions. Thus, the extended model does
a better job of explaining price-cost margins compared to the baseline model. Results
42
using Model 2 are interesting in a number of ways. Primarily, C4 loses all explanatory
power, as well as GR. Yet the coefficient of CI is positive and, to a limited extent,
statistically significant. Most of the coefficients of the interaction terms are
insignificantly different from zero. However, in the OLS results in column 4, the
coefficient of CI*GR is positive and statistically significant, thus indicating that increases
in demand increase price-cost margins in industries with higher capital intensity.
Regarding the coefficient of CI*U, it is negative and statistically significant in the
OLS and random effects regressions. This indicates, for instance, that the positive impact
of the unemployment rate on price-cost margins is lower in industries with higher capital
intensity. Nonetheless, similar to Table 4.1, most of the coefficients of the interaction
terms are insignificantly different from zero. Lastly, as in previous results, the Hausman
test favors the fixed effects over the random effects specifications.
43
Chapter 5
CONCLUSION
5.1
Summary of Findings
This thesis has investigated the impacts of various factors on price-cost margins
of U.S. manufacturing industries over the past several decades. These factors include
several different measures of industry concentration, along with levels of capital intensity,
industry-specific demand, and aggregate demand. The use of interactive variables has
allowed us to examine how changes in demand along with different levels of firm
concentration and capital intensity affect price-cost margins. We also estimated our
models using ordinary least squares, as well as fixed and random effects, to address the
importance of controlling for the panel nature of the data.
The data used for this study were a panel set of U.S. manufacturing industries
under the SIC classification systems obtained from the Census Bureau. A separate data
set using the NAICS classification system was also incorporated. Several of the variables
were constructed using these data and, except for industry concentration, were available
on an annual basis. Industry concentration data were available from 1967 to 1992 on a
five-year basis in the SIC data set (1997 and 2002 for the NAICS data set). When
conducting our statistical analysis, we used data for those years which contained industry
concentration.
44
Two empirical models were constructed for this thesis. Specifically, the first
model treated price-cost margins as a function of industry concentration, capital intensity,
and two measures of demand fluctuations. To construct the second model, we added
several regressors incorporating interactive terms in order to address the influence of
changes in demand. Both models were estimated using several measures of industry
concentration. Furthermore, these models were estimated with ordinary least squares, as
well as industry fixed effects and random effects.
The results show that, almost exclusively, the various measures of industry
concentration have a positive and significant effect on price-cost margins. This is true for
both models using the various estimation methods. The impact of capital intensity is
generally negative and significant in our baseline model. This indicates that industries
with higher barriers to entry have lower profit margins. However, in the extended model
the impact of capital intensity is generally positive. Our growth-in-sales variable is
almost always positive and significant which dictates that price-cost margins increase
when industry specific demand goes up, (i.e., profit margins are pro-cyclical), falling in
line with the hypothesis by Green and Porter (1984). Interestingly, unemployment (a
proxy for aggregate demand) tends to have a positive effect on price-cost margins (i.e.,
profit margins are counter-cyclical), which supports the hypothesis of Rotemberg and
Saloner (1986). Our interactive terms generally show that (i) the impact of concentration
on price-cost margins is largely insensitive to demand fluctuations, (ii) the positive
impact of capital intensity on price-cost margins is dampened during periods of high
45
industry demand, and (iii) the impact of demand fluctuations on price-cost margins is
lower in those industries with higher capital intensity.
5.2
Suggestions for Future Research
With regard to the data from the SIC classification system of manufacturing
industries, it would be useful in future research to incorporate more data to allow the
construction of additional independent variables. Some examples of explanatory
variables which were not used in this thesis include advertising expenditures and import
competition. The research could follow an approach similar to this study, in which
several measures of industry concentration could be used in separate regressions while
incorporating the new variables in the baseline as well as the extended model.
Another suggestion for future research would be to separate the many industries
into sub-groups. Typically, past studies have separated manufacturing industries by
producer-goods industries and consumer-goods industries. It would be interesting to look
at the data in this manner, as well as perhaps more specific sub-groups, such as
manufacturers of semiconductor components.
One aspect of this thesis which presents an excellent opportunity for future
research is that of using NAICS data to conduct similar studies of the determinants of
price-cost margins. As the U.S. Census continues to collect data through their Annual
Survey of Manufactures and Census of Manufactures, more years of data will be
available for future research, and we will be able to investigate if the impacts of industry
concentration and demand fluctuations on price-cost margins are still evident.
46
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