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
___________________
Date iii

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 1997-
2002 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
1
Since profit (π) = P*Q – TC(Q), and for a perfectly competitive firm price is taken as given, the first-order condition for maximizing profit yields 𝛿𝜋 𝛿𝑄
= P-MC = 0, thus 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.

in the market increases, this leads to an increase in profit margins (e.g., see Domowitz,
2
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.

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.
3
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).

the price-cost margin is higher during periods of high demand), which is consistent with
5 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.

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
6 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 1958-
1970. 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

between value-added sales and wages by the value of shipments.
5
Several models were
7 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 1965-
1977 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.

to rely on panel data methods. Specifically, the authors examined price-cost margins for
8
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

findings were that (i) industry margins were pro-cyclical and (ii) industry margins were
10 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:
𝑃𝐶𝑀 𝑖𝑡
= 𝛽 𝑜
+ 𝛽
1
𝐶 𝑖𝑡
+ 𝛽
2
(𝐶𝐼) 𝑖𝑡
+ 𝛽
3
𝐺𝑅 𝑖𝑡
+ 𝛽
4
𝑈 𝑡
+ 𝜀 𝑖𝑡
(1) where PCM it
is the price-cost margin in industry i in year t, C it
is concentration in industry i in year t, and CI it
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 (U t
), which does not vary across industries. The error term is given by ε it
.

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)
13 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):
𝑃𝐶𝑀 𝑖𝑡
= 𝛽 𝑜
+ ⋯ + 𝛽
5
𝐶 𝑖𝑡
𝐺𝑅 𝑖𝑡
+ 𝛽
6
𝐶 𝑖𝑡
𝑈 𝑡
+ 𝛽
7
𝐶𝐼 𝑖𝑡
𝐺𝑅 𝑖𝑡
+ 𝛽
8
𝐶𝐼 𝑖𝑡
𝑈 𝑡
+ 𝜀 𝑖𝑡
(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 (GR it
) acts as an indicator of industry-specific demand fluctuations, while annual unemployment (U t
) 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 That is, from equation (2) we get: 𝛿𝑃𝐶𝑀 𝛿𝐶 𝛽
7
𝐺𝑅 𝑖𝑡
+ 𝛽
8
𝑈 𝑡
= 𝛽
1
+ 𝛽
5
𝐺𝑅 𝑖𝑡
+ 𝛽
6
𝑈 𝑡
and 𝛿𝑃𝐶𝑀 𝛿𝐶𝐼
= 𝛽
2
+
. 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
, while fluctuations in industry-level demand are measured as
𝑇𝑜𝑡𝑎𝑙 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠
𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠 𝑡
− 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠 𝑡−1
.
𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠 𝑡−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
C4
C8
Price-cost margins or profit margins
Four-firm concentration ratio
Eight-firm concentration ratio
H50
CI
GR
U
Herfindahl index (constructed from largest 50 firms)
Capital intensity
Annual percentage change in industry sales
Annual unemployment rate

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
16 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.
PCM
C4
C8
H50
CI
GR
U
Table 3.2 Descriptive Statistics (SIC)
Variable Obs. Mean Std. Dev. Min Max
11934 0.2805 0.0882 -0.0606 0.7927
2255 0.3903 0.2077 0.0100 0.9900
2249 0.5209 0.2309 0.0200 1.0000
1244 0.2323 0.2115 0.0003 0.9997
11934 0.7214 0.5298 0.0570 8.7973
11934 0.0711 0.1355 -0.7686 3.1333
11934 0.0637 0.0159 0.0350 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

three concentration measures. Indeed, it is most pronounced between the four-firm and
17 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
PCM 1
C4 0.1759 1
C4 C8
C8
H50
CI
GR
U
H50 CI GR
0.1422 0.9754 1
0.195 0.9434 0.8851 1
-0.0773 0.1299 0.1677 0.1147 1
0.1233 -0.1106 -0.1326 -0.1058 -0.3629 1
-0.1589 0.0002 0.0075 -0.0039 0.168 -0.3104 1
U
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 .81≤C4≤1.0
1967-1973 0.244 0.261 0.27 0.302 0.34
1974-1981
1982-1992
0.252
0.272
0.27
0.29
0.271
0.299
0.299
0.328
0.309
0.375

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,
18 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.
Figure 3.1 Average Price-Cost Margins
1965 1970 1975
Year
1980 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
1965 1970 1975 1980
Year
1985 1990
1965 1970 1975 1980
Year
1985 1990

20
1965 1970 1975
Year
1980 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

values. As illustrated in Figure 3.5, there is a noticeable positive correlation between
21 concentration and the price-cost margin, particularly since the mid-1980s.
1965 1970 1975
Year
1980
Average PCM
1985
Average C4
1990
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
1965 1970 1975
Year
1980
Average PCM
Unemployment
1985
Average GR
1990
3.3. 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

Manufacturers and the Census of Manufacturers for 473 industries over the 1997-2002
23 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.
PCM
C4
CI
GR
U
Min Max
2838 0.3255 0.0995 0.0650 0.8910
942 0.4229 0.2122 0.0200 1.0000
2838 0.5109 0.3507 0.0710 4.2660
2827 0.0099 0.1645 -0.7166 2.9389
2838 0.0468 0.0058 0.0400 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.

SIC data (.182 compared to .176). There is also strong correlation of capital intensity
24 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
U
PCM
1
C4 CI GR
0.182 1
0.0879 0.208 1
0.0451 -0.0005 -0.2453 1
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.

27

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
28 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
, a higher ratio for a
𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡𝑠 given industry suggests that a new competitor needs to invest a higher amount of capital in order to enter the industry, thus deterring entry.

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
29 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.

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.

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

insignificant, while the coefficient of the unemployment rate remains negative and significant at the 10% level.
36
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.

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.

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

using Model 2 are interesting in a number of ways. Primarily, C4 loses all explanatory
42 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

industry demand, and (iii) the impact of demand fluctuations on price-cost margins is
45 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
REFERENCES
Collins, Norman R., and Lee E. Preston. "Price-Cost Margins and Industry Structure."
Review of Economics & Statistics 51.3 (1969): 271. Print.
Culha, Ali, and Cihan Yalçin. "The Determinants of the Price-Cost Margins of the
Manufacturing Firms in Turkey." Middle East Technical University Studies in
Development 32.2 (2005): 303-31. Print.
Demsetz, Harold. "Industry Structure, Market Rivalry, and Public Policy." Journal of
Law and Economics 16.1 (1973): 1-9. Print.
Dickson, Vaughan. "Price-Cost Margins, Prices and Concentration in Us Manufacturing:
A Panel Study." Applied Economics Letters 12.2 (2005): 79-83. Print.
Domowitz, Ian, R. Glenn Hubbard, and Bruce C. Petersen. "Business Cycles and the
Relationship between Concentration and Price-Cost Margins." RAND Journal of
Economics 17.1 (1986): 1-17. Print.
Domowitz, Ian, R. Glenn Hubbard, and Bruce C. Petersen. "The Intertemporal Stability of the Concentration-Margins Relationship." Journal of Industrial Economics
35.1 (1986): 13-34. Print.
Go, Gerard L., David R. Kamerschen, and Charles D. Delorme, Jr. "Market Structure and
Price-Cost Margins in Philippine Manufacturing Industries." Applied Economics
31.7 (1999): 857-64. Print.
Green, Edward J., and Robert H. Porter. "Noncooperative Collusion under Imperfect
Price Information." Econometrica 52.1 (1984): 87-100. Print.
Haskel, Jonathan, Christopher Martin, and Ian Small. "Price, Marginal Cost and the
Business Cycle." Oxford Bulletin of Economics and Statistics 57.1 (1995): 25-41.
Print.
Kwoka, John E., Jr. "The Effect of Market Growth and Contraction on Industry Price-
Cost Margins." Eastern Economic Journal 16.3 (1990): 221-27. Print.
McFetridge, Donald G. "Market Structure and Price-Cost Margins: An Analysis of the
Canadian Manufacturing Sector." Canadian Journal of Economics 6.3 (1973):
344-55. Print.
Mueller, Willard F., and Maqbool Sial. "Cyclical Variation in the Profit-Concentration
Relationship." Review of Industrial Organization 8.3 (1993): 277-91. Print.

47
Neumann, Manfred, Ingo Böbel, and Alfred Haid. "Business Cycle and Industrial Market
Power: An Empirical Investigation for West German Industries, 1965-1977."
Journal of Industrial Economics 32.2 (1983): 187-96. Print.
Ornstein, Stanley I. "Empirical Uses of the Price-Cost Margin." Journal of Industrial
Economics 24.2 (1975): 105-17. Print.
Prince, Y. M., and A. R. Thurik. "Price-Cost Margins in Dutch Manufacturing: Effects of
Concentration, Business Cycle and International Trade." De Economist 140.3
(1992): 310-35. Print.
Qualls, P. David. "Market Structure and the Cyclical Flexibility of Price-Cost Margins."
Journal of Business 52.2 (1979): 305-25. Print.
Rotemberg, Julio J., and Garth Saloner. "A Supergame-Theoretic Model of Price Wars
During Booms." American Economic Review 76.3 (1986): 390-407. Print.
Sawhney, Pawan K., and Bansi L. Sawhney. "Capacity-Utilization, Concentration, and
Price-Cost Margins: Results on Indian Industries." Journal of Industrial
Economics 21.2 (1973): 145-53. Print.
Schmalensee, Richard. "Collusion Versus Differential Efficiency: Testing Alternative
Hypotheses." The Journal of Industrial Economics 35.4 (1987): 399-425. Print.