Market structures, strategic investment behavior, and profitability

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Market structures, strategic investment behavior, and profitability
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Thesis (Ph. D.)--University of Florida, 1988.
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Includes bibliographical references.
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by Vivek Ghosal.
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MARKET STRUCTURES, STRATEGIC INVESTMENT BEHAVIOR, AND
PROFITABILITY








BY

VIVEK GHOSAL


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY



UNIVERSITY OF FLORIDA


1988


I1 PLDELRORIDA RARIES










ACKNOWLEDGEMENTS

I would like to thank my advisors, Drs. Sanford Berg,

Roger Blair, Leonard Cheng, and John Lynch, for their helpful

suggestions. Special thanks are due to Dr. Cheng for his

encouragement during the course of this study. I have

benefitted from comments by Drs. Stephen Cosslett, David

Denslow, Lawrence Kenny, Richard Romano, Mark Rush, and

Steven Slutsky. Comments on an earlier draft of my second

chapter by Ian Domowitz, Avinash Dixit, and a discussion with

Richard Schmalensee proved to be helpful.

Lastly, and most importantly, I would like to thank

Edward Golding and Prakash Loungani whose constant support

and encouragement enabled me to complete this dissertation.

This research has been supported under a Doctoral

Dissertation Fellowship awarded by the Public Policy Research

Center at the University of Florida. I gratefully acknowledge

funding from the Public Utilities Research Center at the

University of Florida.

I dedicate this dissertation to my parents.











TABLE OF CONTENTS


PAGE

ACKNOWLEDGEMENTS ........................................ii

ABSTRACT................................................ iv

CHAPTER

I INTRODUCTION........ .............. ......... ...... 1

II ADJUSTMENT COSTS OF CAPITAL, PRE-EMPTIVE
INVESTMENTS, AND SELLER CONCENTRATION............... 6

Capacity as an Entry Deterring Instrument.........6
Adjustment Costs of Capital and Seller
Concentration.... .............................10
Demand Conditions and Capital Intensity..........17
Estimation of Adjustment Costs of Capital.........19
Empirical Analysis..............................22

III INTERINDUSTRY AND INTERTEMPORAL ANALYSIS OF
PRICE-COST MARGINS..................................32

Concentration, Profitability, and Unionism:
A Non-Linear Relationship....................... 35
Growth of Monetary Base and Profitability........52

IV CONCLUSIONS AND EXTENSIONS......................... 64

APPENDIX

A DIRECT ESTIMATION OF ADJUSTMENT COSTS......... ......67

B DEMAND UNCERTAINTY AND FACTOR INTENSITY...............74



REFERENCES............................................................ 77

BIOGRAPHICAL SKETCH...................................... 81


iii











Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy



MARKET STRUCTURES, STRATEGIC INVESTMENT BEHAVIOR, AND
PROFITABILITY

By

Vivek Ghosal

August, 1988



Chairman: Dr. L. K. Cheng
Major Department: Economics



Recent models of oligopoly theory have shown that an

incumbent firm, by making a strategic investment commitment

in the pre-entry stage, may deter entry. My key hypothesis

is that adjustment costs of capital will influence an

incumbent firm's decision to install strategic capacity. By

assuming a quadratic and symmetric adjustment cost function,

I looked at the issues related to the credibility and

desirability of strategic investment. I hypothesised that

only over an intermediate range of adjustment costs of

capital will the incumbent firm have an incentive to install

strategic capacity. From this I derived an inverted U-shaped

relationship between adjustment costs of capital and seller

concentration. A cross section study across 125 S.I.C. 3-






digit manufacturing industries provided evidence in favor of

this relationship.

Studies on the relationship between concentration and

profitability have recently highlighted the intertemporal

instability of this relationship. Adopting a methodology by

which we carry out cross section analysis after controlling

for cyclical fluctuations, I failed to find any structural

instability in the relationship. Using a correct non-linear

specification, I show that elasticities between concentration

and profitability have remained remarkably stable over the

period 1968-1977. My results show that concentration and

profitability are positively related only at higher

concentration levels. Also, the results show strong evidence

in favor of a negative relationship between unionism and

profitability, and that this negative effect is to be found

primarily in more oligopolistic industries. My analysis shows

that model specification is an important issue in analyzing

the structure-performance relationship.

To formally analyze the behavior of industry price-

cost margins over business cycles, I looked at the influence

of monetary base expansion on price-cost margins. I found

that the relationship between growth rate of monetary base

and price-cost margins was positive. Furthermore, I found

that more concentrated industries showed stronger pro-

cyclical margins.











CHAPTER I
INTRODUCTION

The structure-conduct-performance paradigm has been

an important framework of analysis in industrial

organization. In this framework an important component of

structure is the number and size distribution of firms.

Conduct, or firm behavior, is assumed to depend on market

structure. Conduct, in the form of pricing strategies and

pre-emptive behavior, in turn influences profitability, or

performance, of firms. The theoretical and empirical works on

the determinants of market structures initially concentrated

on technological characteristics, fixed costs, demand growth,

and some measure of entry barriers, created or natural, as

the key determinants of the size distribution of firms in an

industry. Subsequent works analyzed and tested the role of

advertising intensity, research and development intensity as

being artificially created barriers to entry in concentrated

markets. More recent theoretical work has concentrated on

the role of pricing strategies and investment behavior within

a game theoretic framework as important aspects of firm

behavior. See Scherer (1980), Stiglitz and Mathewson (1986),

and Waterson (1984) for an overview of this literature.

Empirical work has been voluminous and often inconclusive.

See Curry and George (1983) for a survey of the empirical

literature on the determinants of concentration.

1






2

Seller concentration, as measured by four-firm

concentration ratio (CR4), is generally regarded as an

important determinant of business behavior and industry

performance. In the study of seller concentration and

performance there are two fundamental, and distinct,

questions that need to be analyzed: (1) What determines the

level of seller concentration? (2) What are the potential

effects of market power? We analyze both these questions. In

chapter II we focus on the determinants of seller

concentration in light of the new theories of industrial

organization and firm behavior (Dixit 1979, 1980, 1982;

Eaton and Lipsey 1979, 1981; Spence 1977, 1979; and Spulber

1981). The idea in these models is that firm behavior may be

an important determinant of seller concentration. To the

extent that entry is one of the determinants of seller

concentration, entry deterring behavior will influence market

structures. I use the broad framework of the above mentioned

models to analyze strategic investment behavior and its

influence on seller concentration.

I identify parameters that will influence a firm's

decision making with regard to installing strategic

capacity. The theoretical models by Dixit, Spence, and

Spulber ignore the implications of adjustment costs of

capital on a firm's strategic behavior. My key hypothesis is

that adjustment costs of capital will influence an incumbent

firm's decision to install strategic capacity. If the costs







3

of adjusting capital upward are high, strategic capacity

would be costly to install. If costs of downward adjustment

are low, strategic capacity will not serve as an irreversible

commitment. It is only over an intermediate range of

adjustment costs of capital that the incumbent firm will have

an incentive to install strategic capacity.

Although theoretically demand growth is predicted to

be an important determinant of market structures, the

empirical literature has provided inconclusive evidence.

Depending on the sample period and level of aggregation,

studies have shown that demand growth may increase or

decrease seller concentration. In chapter II I present the

various arguments related to demand growth and its

relationship to seller concentration, and note that only in

the absence of pre-emptive behavior will demand growth reduce

seller concentration. The literature has been less rigorous

in analyzing the role of demand uncertainty on seller

concentration. Assuming risk-aversion on part of the

potential entrant, demand uncertainty may lead to lesser

entry. This would imply a positive relationship between

seller concentration and demand uncertainty. I examine this

empirically.

The literature on the relationship between

concentration and profitability has consistently provided

evidence in favor of a positive relationship. The literature

assumes a monotonic relationship between concentration and






4

profitability. Without exception, all early studies were

cross section studies. Recent works by Qualls (1979) and

Domowitz et al. (1986a, 1986b) showed that price-cost margins

show cyclical patterns, and that these patterns vary across

levels of concentration. Domowitz et al. show that cross

section results are misleading, because of the cyclical

nature of price-cost margins (PCM). In chapter III we carry

out a cross section analysis after controlling for cyclical

fluctuations. I show that the evidence presented by Domowitz

et al. (1986a) can be misleading. Controlling for cyclical

variability and using a correct non-linear specification I

show that the elasticities between concentration and

profitability have remained remarkably stable over the period

1968-1977.

I also focus on the issue of unionization and its

effects on profitability. I shall show that the significance

of the unionism effect is dependent on model specification,

and that there is strong evidence of the unionism effect

being found primarily in more oligopolistic markets.

Regarding the analysis of the cyclical nature of PCM,

Qualls (1979) analyzed the volatility of PCM for 79 4-digit

industries and concluded that the cyclical variability of PCM

was positively related to concentration. Domowitz et al.

(1986a) using the economy wide unemployment rate as an

indicator of aggregate demand, showed that PCM were strongly

pro-cyclical in more concentrated industries. Their results,







5

curiously enough, suggest that PCM is counter-cyclical in

less concentrated industries. Schmalensee (1987) points out

that the secular increase in the unemployment rate over the

period 1958-1981 reflects structural changes in the labor

market rather than a trend towards increasing slack in the

markets. Therefore, he concludes that the unemployment rate

is a bad indicator of aggregate demand. He found the

capacity utilization rate to be a better indicator of

aggregate demand.

To analyze the behavior of PCM over business cycles I

considered growth rate of the monetary base as an aggregate

demand influence (in contrast to aggregate demand indicators

used by Domowitz et al. (1986a) and Schmalensee). Increase in

growth of the monetary base will cause an expansion of

demand. Decrease in the growth rate will cause a downturn. I

used current and lagged growth rates of the monetary base to

study PCM movements over business cycles.











CHAPTER II
ADJUSTMENT COSTS OF CAPITAL, PRE-EMPTIVE INVESTMENTS,
AND SELLER CONCENTRATION


Capacity as an Entry Deterring Instrument

Schelling (1960) argued that a threat, which is

costly to carry out, can be made credible by entering into

prior commitment. Recent models of oligopoly theory show that

an incumbent firm, by making an investment commitment in the

pre-entry stage, can change the initial conditions such that

it may alter the post-entry outcome in its favor. Dixit

(1980), using linear demand and cost functions, shows that if

the post-entry game is Nash then firms, by investing in

strategic capacity, may deter entry. In Dixit's model firms

will not hold any idle capacity. Spulber (1981), in a more

general and dynamic model, shows that firms may install

strategic capacity under both Nash and Stackelberg

strategies. Under a Nash strategy there will be no idle

capacity whereas under a Stackelberg strategy, firms may

hold idle capacity. Dixit (1979) and Spence (1977) show that

if the post-entry game is Stackelberg, firms will hold idle

capacity and the threat is one of expanding output in the

face of entry. A Cournot-Nash strategy results in a constant

high output whereas a Stackelberg strategy is one of

underutilized capacity in the pre-entry stage.

The basic idea behind the models can be explained

6






7

with the following three figures. Figure 2.1 examines the

marginal cost and marginal revenue functions for the

established firm, and in Figure 2.2 we get the established

firm's reaction function. Figure 2.3 shows the equilibrium

output levels for the entrant and the established firm under

different reaction functions for the incumbent.







MR1

w+r\ \-1MC
\MR1




x

K1


Figure 2.1: Marginal Cost and Marginal Revenue


In figure 2.1, MC is the established firms marginal cost

schedule. The variable costs associated with labor are

constant at w, and capacity expansion costs equal r. For

capacity choice K1, marginal costs are w till Kl. Beyond K1

capacity expansion costs matter, so marginal costs are w+r.

Let MR1 be the established firm's marginal revenue schedule

when its rival produces no output. An increase in the

rivals' output will shift the established firms marginal

revenue schedule to the left e.g. from MR1 to MR1*.







8

By tracing the movement of the intersection of the

marginal revenue schedule with the marginal cost schedule we

get the established firm's reaction function given in figure

2.2. We get a vertical stretch in the reaction function

corresponding to the vertical stretch in the marginal cost

schedule in figure 2.1.




X2

D


Figure 2.2:


Established Firm's Reaction Function


In figure 2.2, X1 is the established firm's output and X2 is

the entrant's output. Stretch AB represents the capacity

constrained stretch and CD is the stretch where capacity is

not a constraint. If the established firm chooses to install

a larger level of capacity, then the established firm will

have a longer stretch of the unconstrained reaction function.

Thus by choice of capacity in the pre-entry stage, the






9

established firm can present either the capacity constrained

reaction function, R1(C), or a reaction function where

capacity is not a constraint, R1(UNC). In figure 2.3, the

entrant's reaction function is R2. So if the established firm

presents R1(C), then the entrants output is X21. If the

established firm presents Rl(UNC), then the entrants output

is X22. So by choosing a larger capacity in the pre-entry

stage, the established firm forces the entrant to smaller

levels of output. If the residual demand for the entrant

becomes so small that it does not make any positive profits,

then entry will be deterred.




X2
R1(UNC)



1(C)

X21


X22


R2

X1



Figure 2.3: Equilibrium Output



In the oligopoly models that we referred to above,

investment in capacity serves as the entry-deterring





10

instrument. One of the main assumptions of these rmo'lss is

that capital is industry specific. Therefore thase models

assume that there is no downward adjustment of cac-zit,.

Also, they assume that there is an instantaneous upT-aL-d

adjustment of capacity.

Under the assumption of no downward adjust :.-l- of

capacity, installed capacity always represents an

irreversible commitment. The requirement of credibility is

automatically" satisfied. Tnsti~'.taneous uLwa-r adjus*afent of

capacity implies that there are no costs associat- w;i:-

adjusting capacity upwards to entry deterring lIv:.-.



Adjusttnent Costs of Capita! a?.d eller Con2- :-. :._n

In this paper I consider an environment u'-. firms

can adjust capacity, subject to ?fj'stment costs. I

hypothesize that adjustment costs of capital will influence

an incumbent firm's decision to install strategic .-TJhcity.

Adjustment costs will determine whether the insta-'.d

capacity represents an irreversible co-mitment. :c,

adjustment costs of capital will contribute to tr-. total cost

of installing new capacity. High adjustment cost... c c-i.tal

would imply that incumbent firms have little inc.:-.: ive to

install strategic capacity. Gould (1968) and Mus-j :?.;-),

among others, have analyzed the issues related to the

adjustment costs of capital. Two reasons are give.,t as to why

firms would incur adjustment costs of capital: (i) f.r ..rward







11

adjustment of capital there are costs associated with

integrating new capital, training workers etc. (ii) for

downward adjustment of capital, the firm has to devote

resources for dismantling and removing capital, and

reorganizing production schedules. Holt et al. (1960) provide

a suggestive list of adjustment costs that might be incurred

and provide justification for using quadratic cost

functions. Following the standard approach in literature, I

assume that adjustment costs are quadratic and symmetric in

upward and downward adjustment.

If entry deterrence is profitable, the following

inequality must hold.



P(m) C > P(d) (2.1)



where P(m) and P(d) are monopoly and doupoly profits,

respectively. The cost of deterring entry is C. Adjustment

costs of capital will be one of the components of C.

Strategic capacity must (i) be installed prior to entry and

(ii) represent an irreversible commitment (see Dixit (1982)).

Adjustment costs of capital will determine whether

installed capacity represents an irreversible commitment. If

costs of adjusting capacity are low, firms could, fairly

costlessly, adjust capacity downwards. Therefore the

irreversibility condition will not be satisfied. The

irreversibility condition will be satisfied only over a







12

higher range of adjustment costs of capital. The magnitude of

C in inequality 2.1, combined with the irreversibility

condition, will determine whether the incumbent firm has an

incentive to install strategic capacity.

We use figure 2.4 to construct a simple framework

within which we analyze the nature of the relationship

between adjustment cost of capital, strategic investment

behavior, and seller concentration. In figure 2.4 let K(ED)

be the entry deterring level of capital stock and K(M) be

the monopoly level of capital stock. Strategic capacity is

given by K(ED)-K(M).



Capital
Stock



K(ED)

Strategic
Capacity
K(M)










Time
tl(Incumbent t2 t3 T(Expected
Enters) Entry)

Figure 2.4: Strategic Capacity






13

The established firm enters the industry in period

tl. By period t2 it has installed its monopoly level of

capital stock, K(M). If the established firm expects entry at

some time period T and it wants to install strategic

capacity, it must do so before period T (say by period t3,

where the distance between t3 and T can be arbitrarily

small).

The irreversibility of the commitment of installing

strategic capacity, K(ED)-K(M), will be determined by the

costs of adjusting capacity--CK for short. I had earlier

mentioned that adjustment costs are symmetric. This implies

that costs of upward and downward adjustment are the same

i.e. CK. A high cost of adjusting capacity downward would

imply that the commitment is irreversible. However, a high CK

would lead to P(m) C < P(d). This implies that sharing is

the best strategy and the incumbent firm will have no

incentive to install strategic capacity.

If CK is low, then P(m) C > P(d) but strategic

capacity will not serve as an irreversible commitment. A low

CK implies that the incumbent firm could, fairly costlessly,

adjust capacity downwards. The entrant will not perceive

installed strategic capacity as a threat and the incumbent

will have no incentive to install it.

Over the intermediate range of CK we may have

adjustment costs high enough for strategic capacity to serve

as a near-irreversible commitment. At the same time we have






14

CK not high enough to reverse the inequality 2.1. It is over

this intermediate range of CK that the incumbent firm will

have an incentive to demonstrate entry deterring behavior by

installing strategic capacity. There are two counteracting

forces of credibility and desirability. Credibility is an

increasing function of CK and desirability is a decreasing

function of CK. Figures 2.5 and 2.6 show the relationships.



Credibility
of Strategic
Capacity









CK


Figure 2.5: Credibility of Strategic Capacity



Desirability
of Strategic
Capacity









CK


Figure 2.6: Desirability of Strategic Capacity







15

The two counteracting influences of credibility and

desirability determine the likelihood of strategic

investment. The likelihood is low, over low and high values

of CK. The likelihood is high over the intermediate range of

CK. For symmetric adjustment costs, the relationship is shown

in figure 2.7.


Likelihood
of Strategic
Investment









CK



Figure 2.7: Likelihood of Strategic Investment



The relationship as it stands above cannot be tested

because we do not have a measure of likelihood of strategic

investment. To make the relationship testable we use the

concept of barriers to entry. Barriers to entry are high when

likelihood of strategic investment is high. Low and high

values of CK, implying low likelihood of strategic

investment, give us low barriers to entry. Relating market

structures to barriers to entry, we get high seller

concentration when barriers are high. When barriers are low,

we get low seller concentration. So the relationship between







16

seller concentration, and capacity adjustment costs is an

inverted U-shaped. Low and high CK imply low concentration.

Intermediate values of CK imply high concentration. Equation

2.2, table 2.1, and figure 2.8 summarize the relationship

between seller concentration (CR4) and cost of adjusting

capacity (CK).



CR4 = fl( CK : CK2 ) (2.2)
+

Table 2.1
Strategic Investment and Seller Concentration


Strategic Barriers
CK Investment to Entry Concentration

Low Unlikely Low Low

Intermediate Likely High High

High Unlikely Low Low


CR4











CK


Figure 2.8: Adjustment Costs of Capital and Concentration






17

Demand Conditions and Capital Intensity

Nakao (1980), in a model of demand growth and entry,

shows that a high rate of growth of demand will raise the

rate of return on capital and lower entry barriers. High

demand growth, by attracting entry, will lower the

established firms market share. Spence (1979), in a model of

investment strategy in growing markets, argues that just as

potential entrants may be deterred by the capacity that the

established firms have installed, smaller firms may be

deterred from expanding by the existing capacity of their

larger rivals. Eaton and Lipsey (1979) show that a

monopolist could pre-empt the market by installing new

capacity just before a new entrant decides to do so.

The growth model of Nakao and the strategic

investment models have different predictions on the growth-

concentration relationship. If strategic investment succeeds

in deterring rivals from entering or expanding, then demand

growth will not reduce seller concentration. High rates of

growth will reduce the leader firm's ability to pre-empt, but

the theory does not provide us with any priors as to how high

the rate of growth should be. Demand growth will be

negatively related to concentration only if the growth

influences dominate.

The second aspect of demand we consider is demand

volatility (DV). Models of the firm that analyze decision

making under demand uncertainty typically assume risk-







18

aversion (see Leland (1972)). If expected profit equals

certainty profit, then, under risk-aversion, the expected

utility of profits is lower under uncertainty than under

certainty. Looking at it from the entrant's perspective, a

risk-averse entrant is less likely to enter an industry with

high demand uncertainty (see Sandmo (1971)). Thus industries

with high demand uncertainty may attract less entry and

therefore be more concentrated. The relationship between

concentration and uncertainty is likely to be positive.

Lastly we look at the capital-labor ratio. It has

been argued that a high capital-labor ratio (K/L) might imply

high barriers to entry. Therefore K/L and concentration are

likely to be positively related. Some of the recent

literature on contestable markets (see Baumol, Panzar, and

Willig (1980)) has stressed on sunk cost as the true barrier

to entry. But since we do not have a measure of sunk cost, we

use K/L as our measure of entry barrier. Equation 2.3 gives

us our relationships from this section.



CR4 = f2( DG : DV : K/L ) (2.3)
? + +



Combining equation 2.2 and 2.3 we get the

determinants of seller concentration as analyzed in sections

2 and 3 of this chapter:

CR4 = f3( CK : CK2 : DG : DV : K/L) (2.4)
+ ? + +







19

In equation 2.4, CK and CK2 are the variables that capture

the behavioral aspects of the relationship. Demand growth

(DG) and demand volatility (DV) capture the market demand

conditions. In the next section we outline a procedure and

estimate adjustment costs of capital which are required to

test the relationship 2.4.



Estimation of Adjustment Costs of Capital

In section 2 we noted studies by Gould (1968) and

Holt et al. (1960) which have analyzed the various components

of adjustment costs of capital and the justification for

using quadratic and symmetric adjustment cost functions. In

this section we obtain a measure of adjustment costs of

capital (CK), within a partial adjustment framework. Partial

adjustment models, which have their basis in quadratic cost

minimization, along with various expectations schemes, have

been widely used to analyze factor adjustment processes.

Techniques that enable us to directly estimate adjustment

costs are computationally expensive and, more importantly,

beset with estimation problems (see Appendix A).

I adopt a framework of partial adjustment under

rational expectations to estimate the speed of adjusting

capital (see Kennan (1979)). I then use the estimated speed

of adjustment as a proxy for cost of adjustment. If the

estimated speed of adjustment is low (high) then the cost of

adjustment is taken to be high (low). Kennan's model is







20

estimated by ordinary least squares and is ideally suited for

our highly disaggregated study. While Kennan uses his

framework to study labor adjustment, I use the model to

estimate speeds of adjusting capital.

The firm is assumed to be making decisions on the

optimal choice of capital stock such that it minimizes the

expected present value of a quadratic loss function:



minK E Et Rt ( al(Kt K*t)2 + a2(Kt Kt-1)2 ) (2.5)



where R is the known discount factor (following Kennan we

assume R=l). Capital stock in time period t, is Kt. The

stochastic target level of capital stock is K*t. The target

level is related to output, which is the observed exogenous

variable. It can be shown from 2.5 that the optimal path for

capital follows the partial adjustment rule (see Kennan, p.

1443):



Kt Kt-l = 6( dt Kt-1 ) (2.6)



where 6 is the speed of adjustment of capital, and dt is the

long run target level of capital stock. The optimal decision

rule is given by equation 2.7.


Kt = (l-6)Kt-1 + 6dt + et


(2.7)






21

Assuming rational expectations, Kennan shows that if

output follows an autoregressive process of order p, AR(p),

then the long run target, dt, can be replaced by a linear

combination of current and past values of output.

We have 125 S.I.C. 3-digit manufacturing industries

in our sample. Annual data on output were obtained from the

Bureau of Industrial Economics data base. Regressions of

output on lags of output showed that for the majority of

industries one lag of output was significant in determining

current output. So for convenience we assume an AR(1)

process, for output, for all the industries in our sample.

Therefore in equation 2.7 we replace dt by current and one

lag of output. Our estimating equation is



Kt = P1Kt-1 + 32Qt + P3Qt-l + ut (2.8)



where P, = (1-6) and 0 < 6 < 1. As noted earlier, 6 is the

speed of adjusting capital. So ~1 is our proxy for adjustment

costs of capital (CK) and 0 < P/ < 1. Adjustment costs are

negligible (very high) if Pl is close to zero (unity).

Along with output (Q), annual data on gross capital

stocks (K) were obtained from the B.I.E. data base. We have

125 3-digit industries in our sample. All data were measured

in constant (1972=100) dollars. The annual time series is for

the period 1958-1980. Before we estimate equation 2.8, all

data were converted to logarithms and then first difference.






22

Differencing is done to induce stationarity (see Fuller

(1976)). Also, in time series data, a common trend among

explainatory variables is a source of multicollinearity.

Differencing is one of the methods suggested to correct for

this problem (see Maddala (1977)). We estimate equation 2.8

for the 125 industries in the sample.

All our estimates of P1 are within the parameter

bounds. The mean P1 is 0.76 with a standard deviation of

0.18. A high mean P1 is indicative of the fact that there are

high costs of adjusting capital over an annual horizon. Some

of the industries with relatively high adjustment costs are

industrial organic chemicals, reclaimed rubber, iron and

steel foundries, electrical industrial apparatus, meat

products, communication equipment, and railroad equipment

(the corresponding S.I.C. 3-digit codes-are 286, 303, 332,

362, 201, 366, and 374). Some of the industries with

relatively low adjustment costs are weaving mills-manmade

fibres, pulp mills, leather goods, cut stone and stone

products, plumbing and heating (non-electrical), optical

instruments and lenses, and concrete, gypsum, and plaster

products (the 3-digit codes are 222, 261, 319, 328, 343,

383, and 327).



Empirical Analysis

The model to be estimated as derived in section 2 is

CR4 = a0 + alCK + a2CK2 + a3K/L + a4DG + a5DV + vt (2.9)









where

CR4 is the 3-digit four firm seller concentration

CK is the cost of adjusting capacity

K/L is the industry capital-labor ratio

DG is the rate of growth of industry output

DV is the measure of demand uncertainty.

As regards sign prediction, al, a3, a5 > 0 and a2 < 0. The

sign of a4 is ambiguous.

Data on production workers (L) were obtained from the

B.I.E. data base. Data on unadjusted concentration ratio

(CR4) were obtained, at the 4-digit level, from the census of

manufactures. I compute 3-digit concentration ratios as a

weighted (by shipments) average of 4-digit concentration

ratios. Weiss and Pascoe (1986) adjust the census

concentration ratios for geographical fragmentation of

markets, disclosure problems, and import competition. To

estimate equation 2.9 I constructed two samples. Sample 1

covers the period 1958-1972. Since output follows a time

trend, computing the coefficient of variation will not give

us a proper measure of demand volatility. Instead we regress:



LogQ = ag + alt + Et (2.10)



where LogQ is the logarithm of output and t is time. We use

the standard deviation of the residuals from this regression

as our measure of demand volatility (DV). The average annual






24

rate of growth was obtained by continuous compounding over

the sample period. So, from the output time series we get

DG(1) and DV(1). The numbers in parentheses reflect the

sample period. Capital-labor ratio, K/L(1), is the mean ratio

over the sample period. The census concentration ratio for

the first sample, CR4(72), is for the year 1972. The adjusted

ratio is ACR4(72).

Sample 2 covers a slightly longer sample period 1958-

1977. All variables are constructed in the same manner as in

sample 1. So we have DG(2), DV(2), and K/L(2) as our

constructed variables. The adjusted concentration ratio for

the year 1977 is ACR4(77).

We only have one set of estimates for CK, as obtained

in the previous section. The lack of a longer time series in

K and Q has prevented us from estimating different sets of

CK. Seller concentration at any point in time must to some

extent reflect past influence of firm behavior, demand

growth, and demand volatility. It is for this reason that we

use a sufficiently long time period to construct our

variables.

Next we present the results of estimating equation

2.9. Since theory does not provide us with any functional

forms, researchers have used both the levels and a

logarithmic form to estimate the relationships (see Curry and

George (1983), and Waterson (1984, cp 10)). Our logarithmic

form uses logarithms of concentration and capital-labor ratio







25

denoted by LCR4(72) and LK/L, respectively. Means of the

variables and estimates, for samples 1 and 2, are presented

in tables 2.2-2.5.

The t-statistics are in parenthesis. All t-statistics

are computed from heteroskedasticity-consistent standard

errors (see White (1980)). Examining the coefficients in

tables 2.3 and 2.5 we observe that all the coefficients

attached to CK and CK2 are of the right sign and

significant atleast at the 10% level. This provides evidence

in favor of my hypothesis in section 2 where I derived the

inverted U-shaped relationship between concentration and

costs of adjusting capacity (see Figure 2.8).

If we differentiate our concentration measures with

respect to capacity adjustment costs (setting other variables

at their mean values), and equate it to zero, we get a

critical value of CK, CK*. These critical values,

representing the turning points of the inverted-U, are

presented at the bottom of each column.

Referring back to the analysis of section 2, we could

argue that for values of CK greater than 0.72 capacity

expansion costs start becoming too large for installation of

strategic capacity. Around the critical value of 0.72,

capacity adjustment costs are in favor of installation of

strategic capacity. Note that if such strategic behavior was

not prevalent, or if strategic investment did not succeed in

deterring entry, then we would not get the observed










Table 2.2
Sample Period: 1958-1972

Variable Mean Std. Deviation

CR4(72) 0.377 0.1834
ACR4(72) 0.389 0.1669
CK 0.76 0.18
K/L(1) 24.07 32.48
DG(1) 0.038 0.033
DV(1) 0.077 0.054


Table 2.3
Estimates: 1958-1972


DEPENDENT VARIABLE
CR4(72) LCR4(72) ACR4(72) LACR4(72)

Inter 0.04 -2.27 0.07 -2.38
(0.5) (-8.9) (0.8) (-6.4)

CK 0.76 1.72 0.80 1.88
(2.3) (1.8) (2.6) (1.8)

CK2 -0.58 -1.36 -0.58 -1.35
(-1.9) (-1.7) (-2.2) (-1.6)

K/L(1) 0.0008 0.0008
(2.3) (1.6)

LK/L 0.15 0.22
(4.2) (4.9)

DG(1) 0.87 2.54 0.42 0.97
(1.7) (2.1) (0.9) (0.9)

DV(1) 0.78 2.15 0.33 1.20
(2.7) (2.4) (1.3) (1.4)

R2 0.1073 0.1586 0.0861 0.2412

CK* 0.65 0.63 0.69 0.69












Table 2.4
Sample Period: 1958-1977

Variable Mean Std. Deviation

ACR4(77) 0.376 0.1682
CK 0.76 0.18
K/L(2) 27.12 36.12
DG(2) 0.033 0.028
DV(2) 0.105 0.064


Table 2.5
Estimates: 1958-1977

DEPENDENT VARIABLE
ACR4(77) LACR4(77)


-2.49
(-0.35)

1.72
(1.51)

-1.18
(-1.31)


0.078
(0.9)

0.75
(2.46)

-0.52
(-1.92)

0.0007
(1.57)


Inter


CK


CK2


K/L(2)


LK/L


DG(2)


DV(2)


R2

CK*


0.184
(0.34)

0.72
(0.86)

0.0733

0.72


0.242
(4.7)

0.377
(0.31)

0.73
(1.86)

0.2399

0.73







28

relationship between seller concentration and capacity

adjustment costs.

Contrary to the predictions of Nakao's model, the

coefficient on demand growth, DG, is positive in all the

equations. However, it is significant only in the first two

columns of table 2.3. This provides further evidence against

the traditional reasoning that demand growth will reduce

seller concentration and help correct for some of the

problems related to market power. In all the equations

presented, the coefficient of capital-labor ratio, in level

or logarithmic form, is positive and significant. The

coefficients of the log-form equations show considerably

larger significance levels.

The coefficient on demand volatility, DV, is always

positive and generally significant. This provides evidence in

favor of the risk-aversion hypothesis we summarized in

section 3, that industries with high demand volatility will

attract less entry and be more concentrated. Finally we note

that the levels equations have pitifully low R2s. This is in

conformity with the general findings in the literature (see

Curry and George (1983)). The log-form equations show

considerably higher explainatory power. The two functional

forms, however, are not comparable. Unfortunately the

theoretical literature has little to offer regarding

functional forms.








29

At the mean values of capital-labor ratio, demand

growth, and demand volatility we get the following

relationships:



ACR4(72) = 0.1311 + 0.80*CK 0.58*CK2

ACR4(77) = 0.1784 + 0.75*CK 0.52*CK2



The plots of the above relationships are shown in figures

2.10 and 2.11.

In summary we note that our empirical analysis has

provided evidence in favor the hypothesis that capacity

adjustment costs are an important determinant of strategic

investment behavior. Evidence at the 3-digit level shows that

pre-emptive behavior may be a feature of the American

manufacturing sector. Finer tests, at the 4-digit level are

likely to reveal more information. The lack of a consistent

data base is a serious problem for analysis at a more

disaggregated level. Also, at this stage, we have not been

able to directly estimate adjustment costs because of

problems related to computational complexity and availability

of large data bases. Finally, our study does not include

variables like research and development intensity and

advertising intensity. Omission of these variables may result

in estimation biases and a possible reduction in the

explanatory power of our estimated equations.














AC7Z I
S0 A AEHL3
0.400 + FFE
I A JG
I AA El
I A3 ME
0.375 + A B
1 A A
I B3
SA A
0.350 + A
A
A
I A
0.325 + A
I
I

0.300 +
I
I B
I
0.275 +
1
I
I
0.250 +

I
I A
0.225 +
I
I

1
0.200 +



0.175 +
I
I
I
0.150 +
I A


0.125 +
I
0.125 +

---------+-----------------------+--- +----
0.0 C.2 0.4 0.6 0.8 1.0 1.2


Figure 2.9: Plot of AC72*CK


Legend: A=1 obs, B=2 ObSI,..













AC77 I
I
0.450 + A AEHLH
D FEJ
LI
I A MG
0.425 + AA
A3 C3
SA A
SA A
0.400 +
A
I
I A
0.375 + A
I A
I A
I
0.350 +
I
I
I
0.325 + B
I
I

I
0.300 +
I
I

0.275 + A
I
I
I
0.250 +
1
I
I
0.225 +
I
I
I
0.200 +
IA

I
0.175 +

-+------------------+---------+--------------------------------
0.0 C.Z 0.4 0.6 0.3 1.0 1.2


Figure 2.10: Plot of AC77*CK


Legend: A=I obs, B=2 obs ....










CHAPTER III
INTER-INDUSTRY AND INTERTEMPORAL ANALYSIS OF PRICE-COST
MARGINS

Empirical analysis of the structure-performance

relationship has been one of the central issues in industrial

organization since the seminal work by Bain (1951). In

general, performance is measured by the markup of price over

average variable cost, (P-AVC)/P, and structure is

represented by the four-firm concentration ratio.

Microeconomic models tell us that firms in oligopolistic, or

monopolistic, markets will enjoy a greater markup as compared

with firms in competitive markets. Until recently, empirical

work concentrated on cross section studies to test whether

more concentrated market structures lead to higher price-cost

margins (PCM). Regressions of PCM on four-firm seller

concentration (CR4), among other variables, showed that the

coefficient of concentration was positive and significant.

This evidence in turn was used by policymakers to support

antitrust regulations. See Bain (1951), Collins and Preston

(1969), Comanor and Wilson (1967), Domowitz et al. (1986a,

1986b), Scherer (1980, p.267-280), and Waterson (1984, cp.2

and 10) for an overview and details on the structure-

performance studies.

The first study that provided evidence against the

validity of the cross section results was by Qualls (1979).






33

Quails highlighted the cyclical nature of PCM and that the

cyclical nature differed across levels of concentration.

Studies by Domowitz et al. (1986a), using a longitudinal data

base covering 284 S.I.C. 4-digit industries for 24 years,

showed the instability of cross sectionally estimated

equations. Regressing PCM on CR4 and capital-output ratio

over the period 1958-1981, they showed that the coefficient

on concentration was not stable over time. There had been a

steady decline in the coefficient since 1963.

The basic message of Domowitz et al. was that cross

sectional inter-industry estimation of the PCM-CR4

relationship may give us misleading results. Despite the

evidence presented by them one can raise three questions

about their analysis. First, concentration data are published

about every 5 years. No continuous time series data are

available on CR4 from the census of manufactures. Domowitz et

al. get over this problem by creating a time series in CR4

by interpolating. Domowitz et al. did not consider the

possibility that CR4 might fluctuate cyclically. If firms in

an oligopolistic industry have idle capacity and this

unutilized capacity is unevenly distributed across firms,

then the market shares of firms are likely to change over the

course of business cycles. If idle capacity is concentrated

in the larger firms, then CR4 is likely to be pro-cyclical.

To the extent that CR4 is actually changing over the course






34

of business cycles, the results of Domowitz et al. need to be

interpreted with caution.

Second, the model Domowitz et al. used to generate

the results on instability is misspecified. Important

variables, which were significant in other equations

estimated by Domowitz et al., were omitted. In the presence

of omitted variable bias, their conclusions lack credibility.

Third, the traditional question in the structure-performance

relationship is whether, on an average, more concentrated

industries show higher price-cost margins. To concentrate on

this traditional question in a cross section framework, we

should analyze the relationship after controlling for

cyclical variability. The cyclical variability of PCM and

how this affects the relationship between PCM and CR4 is a

different question. To analyze this question, one needs

information on cyclical patterns of CR4, cyclical behavior of

PCM, and whether industries show different patterns of

cyclical movements in CR4 and PCM.

The aim of this chapter is twofold. First, in section

1, I shall carry out a cross section study after controlling

for cyclical variability. I do this by considering 5-year

sample periods, and all variables used in estimation are

averages over the sample period. I estimate both linear and

non-linear functional forms to test whether the relationship

between concentration and profitability is monotonic or non-

linear. Recently, there has been some controversy over the






35

effects of unionism on profitability. I examine this linkage

and provide further evidence. Lastly, I address the parameter

instability issue raised by Domowitz et al. (1986a) by

examining both coefficient estimates as well as elasticities

between CR4 and PCM.

Second, in section 2 I shall look at the

intertemporal behavior of PCM. The 125 industries are

classified into 7 different concentration classes to reflect

their levels of competitiveness. After this I analyze time

trends and volatility around trend for all the 7 classes.

Finally, the rate of growth of monetary base is used as an

aggregate demand influence for studying the behavior of

price-cost margins over business cycles. This is in contrast

to Domowitz et al. (1986a) who used the economy wide

unemployment rate as an indicator of aggregate demand. Their

results show that more concentrated industries experience

pro-cyclical PCM. However, their results suggest counter-

cyclical PCM for less concentrated industries. Qualls (1979),

analyzing the volatility of PCM around a linear trend,

provides evidence in favor of pro-cyclical margins.



Concentration, Profitability, and Unionism: A
Non-Linear Relationship

Empirical studies that have analyzed the

concentration-profitability relationship have shown that

concentration is one of the important determinants of

profitability. A linear relationship is postulated between






36

concentration and profitability. One question to ask in this

context is whether the assumption of monotonicity is

realistic. At the lower end of concentration, do even small

changes in concentration imply larger profits? Or is it that

the relationship between concentration and profitability is

positive only at higher levels of concentration? I estimate

both linear and non-linear functional forms to examine this

issue.

Recent works by Freeman (1983) and Salinger (1984)

show that there is a negative effect of unionism on

profitability and that this negative effect is found mainly

in more concentrated industries. Domowitz et al. (1986b), by

assigning 3-digit unionism data to their component 4-digit

industries, showed that the coefficient of unionism was

negative and significant. However, the coefficient of the

concentration-unionism interaction term was positive and

insignificant. Freeman and Medoff (1979) compute unionism

estimates as the percentage of total workers covered by union

bargaining agreements. The data are at the 3-digit level and

the percentages are averages over the years 1968, 1970, and

1972. Domowitz et al. (1986b) treat the unionism figures as a

constant effect over the period 1958-1981. Here I carry out a

study at the 3-digit level for two sample periods, 1968-1972

and 1973-1977, to provide further evidence on the

relationship between concentration, profitability, and

unionism.






37

To analyze inter-industry differences in PCM I

constructed a data base consisting of 125 industries.

Following Domowitz et al. (1986a), I computed PCM as PCM =

(VS + dl PR CM)/(VS + dl), where VS is value of sales, dl

is change in inventories, PR is payroll, and CM is cost of

materials. This definition of PCM is identical to (Value

Added PR)/(Value Added + CM), given the census definition

of value added. This definition is close to the markup ratio

defined as (P-AVC)/P, where P is price and AVC is average

variable cost. Data for all the variables were obtained from

the B.I.E. data base.

As noted earlier, my two samples cover the period

1968-1977. To get some idea about time trends of price-cost

margins I regressed PCM on a constant and a linear trend.

There were 81 industries with a positive trend coefficient.

For this group, the mean trend was 0.0034 with a standard

deviation of 0.0028. The remaining 44 industries showed a

negative trend coefficient. The mean trend coefficient for

this group was -0.0069 with a standard deviation of 0.0228.

For the 125 industries as a whole, the mean trend coefficient

was -0.0002 with a standard deviation of 0.0145. To check

whether there was any relationship between the level of

concentration and trend, I regressed the trend coefficient

on a constant and the level of concentration. The coefficient

was positive (0.0018) but insignificant at standard levels






38

(t-statistic = 0.23). This implies that there was no

difference in PCM trends across levels of concentration.

In my analysis I consider four explainatory

variables: capital-output ratio (K/Q), the adjusted four-firm

concentration ratio (ACR4), industry demand growth (DG), and

the degree of unionization (UN). I also consider the

interaction term between ACR4 and DG, and that between ACR4

and UN. The interaction terms will capture any differential

effects in demand growth and unionization by levels of

concentration. Finally, I use a squared concentration term to

examine whether the relationship between concentration and

profitability is linear or non-linear.

Annual data on gross capital stocks (K) and output

(Q) were obtained from the B.I.E. data base. The four-firm

adjusted concentration ratios are from Weiss-and Pascoe

(1986). The annual rate of growth of output was computed by

continuous compounding. This was our measure of demand

growth. Data on unionism, at the S.I.C. 3-digit level, are

from Freeman and Medoff (1979). Since unionization data were

available only for 117 industries, our sample size was

reduced to the same number. I estimate three functional forms

as given by equations 3.1, 3.2, and 3.3. Other than

differences in levels of aggregation and methodology,

equation 3.1 is similar to that estimated by Collins and

Preston (1969). Equation 3.2 is a more complete specification

and it includes the effects of demand growth and unionism.







39

Finally, in equation 3.3 I add a squared concentration term

to test for non-linearities in the relationship.



PCMi = 30 + PjACR4i + P2K/Qi + ui (3.1)



PCMi = a0 + alACR4i + a2K/Qi + a3DGi + a4UNi + a5ACR*DG

+ a6ACR*UN + ei (3.2)



PCMi = 60 + 61ACR4i + 62ACR42 + 63K/Qi + 64DGi + 65UNi

+ 66ACR*DG + 67ACR*UN + vi (3.3)

i=1,2,....,117



The coefficient on concentration is expected to be

positive. Regarding ACR42, we expect it to be non-negative.

At the least we expect the relationship between concentration

and profitability to be monotonic. However it is possible

that the positive relationship shows up only above a certain

concentration level. In this case the concentration level

coefficient may be insignificant, but the coefficient

attached to the squared term will be positive and

significant. Schmalensee (1987) interprets the coefficient of

K/Q as the average competitive rate of return on capital plus

the average annual depreciation rate, giving it a positive

sign. Studies by Freeman (1983) and Salinger (1984) show that

there is a negative effect of unionism on industry

profitability and that this negative effect is found






40

primarily in more concentrated industries. Thus the

coefficients of the unionism term and the concentration-

unionism interaction term are expected to be negative. The

effect of demand growth on PCM is not very clear. Holterman

(1973) argues that there are entry and production lags. This

would imply that demand growth increases the PCM of the

established firms. Caves (1972) argues that the relationship

between demand growth and profits might be negative because

of competitive forces. In general there is no consensus on

the sign of the demand growth term (see Waterson (1984, p.

197).

I constructed two samples for my analysis. Sample 1

covers the 5-year period 1968-1972. PCM is defined as the

average PCM over the sample period and is denoted by PCM(1).

The average capital-output ratio and rate of growth of output

for sample 1 are K/Q(1) and DG(1), respectively. The

adjusted concentration ratio for the year 1972 is ACR4(72).

Unionism data are the average percentage of workers covered

by collective bargaining agreements over the years 1968,

1970, and 1972. So we treat this as being constant over our

sample period. Sample 2 covers the 5-year period 1973-1977.

All variables are constructed as above. We have PCM(2),

K/Q(2), DG(2), and ACR4(77) for the second sample.

Concentration measure was for the year 1977 and UN data is

the same as in sample 1.






41

Earlier we had noted the mean PCM trends for the

industries in our sample. The small trend component

justifies the procedure of averaging variables over our

sample periods. As mentioned before, averaging of variables

eliminates the cyclical components and enables us to examine

the structural relationship between concentration and

profitability. In terms of economic conditions, sample 1 can

be considered a relatively "good" period and sample 2 a

relatively "bad" period. Sample 1 represents a period when

the economy was at the end of a long period of steady growth.

Our second 5-year period was characterized by an economic

slow down due to oil price shocks. This period was also

characterized by increasing import competition, mainly from

Japan. Tables 3.1 and 3.2 present some summary statistics

and estimates for sample 1. The t-statistics, computed from

heteroskedasticity-consistent standard errors, are in

parentheses.

Examining the results in column 2 we find the

coefficients of ACR4 and K/Q(1) are positive and highly

significant. This confirms the standard finding in

literature. The coefficient on DG(1) is negative and

insignificant at the 10% level. The coefficient on

concentration-growth interaction term is positive and

significant. Differentiating PCM(1) with respect to DG(1) we

get dPCM(l)/dDG(1) = -0.154 + 1.096ACR. At the mean value of

ACR4(72) we get dPCM(l)/dDG(l) = 0.2716. At all values of











Table 3.1
Sample Period: 1968-1972

Variable Mean Std. Deviation

PCM(1) 0.2689 0.0832
ACR4(72) 0.3884 0.1646
K/Q(1) 0.4917 0.3768
DG(1) 0.0184 0.0529
UN 0.4443 0.2329


Table 3.2
Estimates: 1968-1972

DEPENDENT VARIABLE
PCM(1) PCM(1) PCM(1)

Inter 0.185 0.187 0.233
(11.63) (7.48) (6.47)

ACR4 0.170 0.243 -0.056
(3.31) (3.50) (-0.29)

ACR42 0.405
(1.80)

K/Q(1) 0.035 0.044 0.047
(1.97) (2.51) (2.78)

DG(1) -0.154 -0.031
(-0.68) (-0.31)

UN -0.022 0.004
(-0.35) (0.08)

ACR*DG 1.096 0.743
(1.84) (1.10)

ACR*UN -0.173 -0.246
(-1.03) (-2.05)

Adj-R2 0.1388 0.2121 0.2311
E 0.2455 0.2701 0.2353














PCM72 1
0.42 +
I A
I


0.40 +
I A
I

0.38 + A
I

I
0.36 +
IA



0.34 +
I A
IA
I A
IA
0.32 +
A
I A
I A
A
0.30 +
1 A
BB

I BB


DD
I CC
F3
0.26 + ACA
I B
I BD
I B BADE
I 23AC3A2ES C5
0.24 +
1
-+------- -------c-----+---------------
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.35

AC72


Figure 3.1: Plot of P?'I72*AC72


Legend: A=1 obs, B=2 obs,...






44

ACR4(72) above 0.1405, dPCM(1)/dDG(1) is positive. This

implies that for the majority of the industries demand growth

increases profitability. The positive influence of demand

growth on profitability is stronger for more concentrated

industries.

The coefficient on unionism is negative as predicted,

but insignificant. The coefficient on the concentration-

unionism interaction term is also negative and insignificant.

This implies that unionism, for this sample period, is

insignificant in explaining inter-industry differences in

price-cost margins. Differentiating PCM with respect to UN

we get dPCM(1)/dUN = 0.022 0.173ACR < 0. This implies

that across the entire range of concentration, we get a

negative relationship between unionism and margins. The more

concentrated the industry, the larger is the negative effect

of unionism on price-cost margins. However, since the

coefficients are insignificant, this provides weak evidence

in favor of Freeman (1983) and Salinger (1984). Our

conclusions are similar to those of Domowitz et al. (1986b).

To examine the presence of non-linearities we now

turn to the results in column 3. The coefficient of ACR4 is

negative but insignificant. However, the coefficient of

squared concentration is positive and significant. This

implies a significant non-linear relationship and that

concentration and profitability are positively related only

above a threshold concentration level. Furthermore, we note






45

that the introduction of ACR42 has rendered the ACR*DG

coefficient insignificant. The coefficient of ACR*UN is now

significant. The negative and significant coefficient of

ACR*UN provides strong evidence in favor of Freeman (1983)

and Salinger (1984). Finally, differentiating PCM(1) with

respect to ACR4 and evaluating the derivative at the mean

values of ACR4, DG, and UN we get dPCM(l)/dACR4 = 0.1629.

Therefore, at mean values, the relationship between

concentration and profitability is positive. Considering

column 3, at the mean values of K/Q, DG, and UN we get

PCM(1) = 0.2572 0.1517ACR4 + 0.405ACR42. The deravative of

PCM(1) with respect to ACR4 is zero at ACR4=0.1873. So

according to our estimates in table 3.2, at the mean values

of K/Q, DG, and UN the relationship between concentration and

profitability is negative for ACR4 less than 0.1873. If we

plot the above relationship between PCM and concentration, we

get figure 3.1. Over the concentration range 0 < AC72 < 0.27

(approximately), concentration does not seem to have any

positive relationship with profitability. Above AC72=0.27,

however, there is a significant positive relationship. The

bottom flat portion of figure 3.1 contains 31 industries, or

26.5% of the industries in our sample. In conclusion, we note

that for the 1968-1972 sample there is strong evidence in

favor of a non-linear relationship between concentration and

profitability and that ignoring this relationship can lead

to substantial omitted variable bias.






46

Next we carry out a similar exercise with sample 2

which covers the period 1973-1977. Table 3.3 presents the

summary statistics and table 3.4 presents the estimates of

equations 3.1, 3.2, and 3.3 using sample 2. As noted

earlier, the unionism data are the same for the two sample

periods. The t-statistics are in parentheses. All t-

statistics are computed using heteroskedasticity-consistent

standard errors.

Concentrating on our main interests, in column 3, we

note that as before the coefficient of ACR42 is positive and

significant. The coefficient of ACR42 confirms the earlier

finding that concentration and profitability are positively

related only at higher concentration levels. We also note

that the coefficient of the concentration-unionism

interaction term is significant. In comparison to column 2,

the significance level dramatically improves in column 3.

Strangely enough the coefficient of UN now is positive and

significant. Differentiating PCM(2) with respect to UN we get

dPCM(2)/dUN = 0.076 0.397ACR. Thus dPCM(2)/dUN is negative

over all values of ACR greater than 0.1914. The higher is

concentration the more pronounced is the negative effect of

unionism on profitability. However, the positive relationship

between unionism and profitability at ACR4 below 0.1914 seems

puzzling.

The coefficient of the ACR*DG term is positive and

significant. The coefficient of DG is negative and close to












Table 3.3
Sample Period: 1973-1977

Variable Mean Std. Deviation

PCM(2) 0.2679 0.0995
ACR4(77) 0.3727 0.1656
K/Q(2) 0.5220 0.3875
DG(2) 0.0097 0.0404
UN 0.4443 0.2329




Table 3.4
Estimates: 1973-1977

DEPENDENT VARIABLE
PCM(2) PCM(2) PCM(2)


0.167
(5.96)

0.302
(4.37)


0.188
(8.70)

0.163
(2.95)


Inter


ACR4


ACR42


K/Q(2)


0.208
(5.62)

0.018
(0.09)

0.401
(1.76)

0.046
(3.17)

-1.029
(-1.38)

0.076
(1.50)

2.750
(1.73)

-0.397
(-3.57)

0.1359
0.2324


0.046
(3.05)

-1.109
(-1.47)

0.038
(0.58)

2.970
(1.85)

-0.295
(-1.66)

0.1241
0.2779


DG(2)


ACR*DG


ACR*UN


Adj-R2
E


0.036
(2.73)


0.0851
0.2267














PCH77 I
0.450 + A
I
I
I

0.425 +
I
I


0.400 +
I A
I

I A
0.375 +

B
I A
I
0.350 + A
I B


I AB
0.325 +
1 B
I A
I

0.300 + B
I C
I C
A
ND
0.275 + E
I C
BC
I AD
I ID
0.250 + A DHC
I BBCC3FEE
I


0.225 +

-+-- -----------------------------------------
0.0 0.2 0.4 0.6 0.8 1.0 1.2

AC77


Figure 3.2: Plot of PCM77*AC77


Legend: A=l obs, B=2 obs,...






49

being significant. Differentiating PCM(2) with respect to

DG(2) we get dPCM(2)/dDG(2) positive for ACR4 greater than

0.3742. This indicates that more competitive industries

experience a drop in PCM with demand growth. Industries that

are more concentrated experience an increase in PCM with

demand growth. It is difficult to make any definite

conclusions on this result but it seems that the presence of

non-competive forces in the more concentrated industries may

be the primary factor that is driving this result. Pre-

emptive behavior by dominant firms, of the kind analysed by

Spence (1979) and Eaton and Lipsey (1979), would imply less

entry as compared to a competitive market. This would mean

that established firms enjoy larger profits in the face of

growing demand. Lastly, at the mean values dPCM(2)/dACR4=

0.1672. This represents a 2.6% increase over the previous

sample value of 0.1629. At the mean values of K/Q, DG, and

UN we get PCM(2) = 0.2558 0.1317ACR4 + 0.401ACR42. This

result is similar to the results from sample 1. As before, we

plot profitability and concentration to get figure 3.2 (next

page). For concentration range 0 < AC77 < 0.24, we do not

observe any positive relationship. Only for AC77 > 0.24 we do

observe a significant positive relationship. The bottom flat

section in figure 3.2 contains 28 industries, or 24% of the

industries in the sample. As in the first sample, the

significance level of the ACR*UN interaction term improves

considerably with the inclusion of ACR42. Regarding






50

unionism, the evidence is strongly in favor of the negative

effect being found primarily in the more oligopolistic

industries.

Now let us examine the issue raised by Domowitz et

al. (1986a) about the instability of the concentration-

profitability relationship. By looking at the results in

column 1 of tables 3.2 and 3.4. we notice that the

coefficient of concentration is about 4% smaller for the

second sample period. This is in sharp contrast to a 33%

decline in the concentration coefficient between 1972 and

1977 from the estimates of Domowitz et al. (1986a). It must

be noted, however, that the DHP estimates are for the 4-digit

level (compared to our 3-digit level).

Since there are interaction terms in equations 3.2

and 3.3, it will be useful to compute elasticity of PCM with

respect to ACR4 at the mean values of the variables. This

will give us the responsiveness of PCM to changes in

concentration. To compute elasticities we differentiate PCM

with respect to ACR4 and then compute elasticity at the mean

values of the variables. The elasticities, E, are presented

in the last row of tables 3.2 and 3.4. For the first

specification we observe a 7.6% reduction in elasticity.

However this is a misspecified model. For our complete

specification, in the last column, we find a 1.2% reduction

in the concentration elasticity of profitability. This is a

small decrease and we conclude that there was structural






51

stability in the relationship between concentration and

profitability. The main conclusions from our analysis in this

section are:

(1) The presence of a significant non-linear relationship

between concentration and profitability. We find the

relationship to be significantly positive at higher

concentration levels. Not only is this non-linearity

important by itself but also on its influence on other

variables, especially unionism.

(2) It is important to look at elasticities between

concentration and price-cost margins. Using a complete non-

linear specification we show that there is structural

stability in the CR4-PCM relationship over the period 1968-

1977. Also, using results from misspecified models, as in

Domowitz et al. (1986a),.to analyze the stability of a

relationship is likely to be misleading. It is interesting to

note that Domowitz et al. (1986a), in their pooled

estimation, used and found interaction terms and other

variables like import competition and advertising intensity

to be important. Yet they do not present annual estimates for

this specification to show instability.

(3) Our results show strong evidence in favor of the

negative effect of unionism (on profitability) being found

mainly in the more oligopolistic industries. Furthermore, a

correct non-linear specification considerably improved the

significance of unionism effect.









Growth of Monetary Base and Profitability

In this section I set up a framework within which I

analyze cyclical fluctuations in industry price-cost margins

(PCM). I used the growth rate of the monetary base as an

indicator of aggregate demand changes. The growth rate of

industry output is used as a measure of local demand

influence.

The empirical literature on cyclical variability of

PCM is very scanty. At a disaggregated level, Domowitz et al.

(1986a), Quails (1979), and Schmalensee (1987) are probably

the only rigorous works in this area. Domowitz et al. use the

contemporaneous economy wide unemployment rate as their

indicator of aggregate demand conditions. The Domowitz et al.

study covers 284 4-digit industries over the period 1958-

1981. Their results are somewhat intriguing. Their estimated

coefficients suggest counter-cyclical PCM at low levels of

concentration. As the level of concentration rises, PCM

becomes pro-cyclical. While the latter result is expected,

the former result needs explanation. Schmalensee (1987)

concludes that both unemployment and GNP were less useful in

analyzing cyclical variability of PCM than capacity

utilization rate. Quails (1979) summarizes the basic

theoretical issues related to oligopolistic coordination and

price fluctuations and provides empirical evidence on these

issues. Qualls' main finding is that there is a positive

relationship between PCM fluctuations and concentration.







53

Quails' study also shows that relative to trend value, PCM

is compressed more in recessions and expand more in

expansions for highly concentrated industries. Quails uses a

data base covering 79 4-digit industries over the period

1958-1970.

I develop a framework where 3-digit industries are

classified into 7 concentration classes. The concentration

range and classification are shown in table 3.5.



Table 3.5
Concentration Classification


Concentration Range Classification

0 5 ACR4 < 20 Cl
21 5 ACR4 5 30 C2
31 5 ACR4 : 40 C3
41 5 ACR4 5 50 C4
51 < ACR4 < 60 C5
61 : ACR4 5 70 C6
71 ACR4 i 100 C7




The Weiss-Pascoe adjusted concentration ratios are denoted by

ACR4. We use broader concentration range for Cl and C7

because there were very few industries in the concentration

range below 10 and above 80. The mean values of the 1972 and

1977 concentration ratios were used to categorize the

industries. The classes are in decreasing order of

competitiveness. The most competitive group is Cl and C7 is

the least competitive group. All of our experiments were

based on analyzing the mean PCM of each group. The analysis






54

of price fluctuations in oligopolistic markets has focused

on the role of interfirm information flows, threat of

retaliation, and uncertainty. Bain (1950) argued that high

concentration would lead to better interfirm information

flows and this would be conducive to collusion. These

information flows would facilitate the maintanence of a

larger price markup above costs. Qualls (1979) argued that

these same information flows will allow firms to vary the

markup over business cycles without a breakdown of interfirm

coordination. In short, this line of reasoning implies that

highly concentrated industries would succeed in maintaining

higher markups and that these markups will be pro-cyclical.

Industries with moderate to low concentration, or

weak oligopolies, are expected to face problems of interfirm

coordination. Increase in the number of decision making

firms, incomplete information flows, and uncertainty about

rivals' actions have been argued to have a stabilizing effect

on prices. Stable prices in turn would imply stable PCM over

business cycles.

In a purely competitive market, prices will fall

along the short run marginal cost schedule when demand falls.

During an expansion, prices will rise along the marginal cost

schedule. This implies volatility of prices and PCM over

business cycles.

Summarizing the arguments from the existing

literature we get prices and PCM being pro-cyclical in both







55

perfectly competitive and highly concentrated industries

(this, however, precludes entry and exit for short run

changes in demand). Prices and PCM are likely to be more

stable for market structures characterized by low to moderate

concentration. If we observe the complete spectrum of market

structures, perfect competition to monopoly, then the

relationship between PCM volatility and concentration would

be U-shaped. However, if we do not observe perfectly

competitive markets then the relationship will be positive.

It has been argued that the degree of atomism required for

perfectly competitive markets may not exist. As we have

already noted, Qualls found the relationship between PCM

volatility and concentration to be positive.

Since our primary aim was to study PCM movements over

business cycles, we analyzed PCM movements after detrending.

We denote the price-cost margins for our 7 classes by PCMC1

to PCMC7. The time trends and volatility around trend of each

group is presented in table 3.6. The t-statistics are in

parentheses. The volatility of PCM is measured by the root

mean square error (RMSE) from the regression of PCM on a

constant and linear trend.

The correlation between concentration class and time

trend is 0.302 but insignificant at the 10% level. This

implies that there are no significant differences in time

trends across levels of concentration. The correlation

between RMSE and concentration class is 0.798 and is






56

significant at the 5% level. For classes 1 to 4 there seems

to be no relationship between RMSE and concentration class.

RMSE is significantly greater for classes 5 to 7. This is in

conformity with Quails' findings that more concentrated

industries show greater PCM volatility.



Table 3.6
PCM Time Trends



Dep Var Inter Time Adj R2 RMSE

PCMC1 0.186 0.0025 0.8765 0.0063
(67.8) (12.5)

PCMC2 0.214 0.0020 0.8533 0.0057
(85.8) (11.3)

PCMC3 0.229 0.0029 0.8793 0.0063
(63.7) (11.8)

PCMC4 0.241 0.0014 0.7090 0.0061
(92.0) (7.38)

PCMC5 0.268 0.0028 0.8452 0.0083
(74.8) (11.0)

PCMC6 0.261 0.0022 0.7265 0.0089
(67.7) (7.7)

PCMC7 0.312 0.0032 0.6452 0.0159
(45.4) (6.4)


To return to our main analysis, we need a variable

that can be used as an aggregate demand influence. We used a

monetary aggregate, the rate of growth of monetary base

(DBASE). See Barro (1987, cp. 15) and Rush (1986) for

details on the use of monetary base as a demand influence.






57

Increase in rate of growth of monetary base will, via the

multiplier effect, stimulate aggregate demand. Decrease in

rate of growth will deflate demand. Monetary base is defined

as currency in circulation plus the reserves held by the

financial intermediaries at the FED. The FED uses open

market operations as the principal instrument for controlling

monetary base. Regarding neutrality of money, a one time

shift in the monetary base will induce proportional response

in wages, prices, and all nominal variables. This will leave

real variables like quantity of output and employment

unchanged.

If all nominal variables adjust instantaneously then

even in the short run there will be no real effects. However,

Fischer (1977) and Phelps and Taylor (1977) have shown that

wage contracting results in nonneutrality. Mishkin (1982)

provides evidence that even anticipated changes in money

matter. In general, the short run effects of money growth

will depend on the nature of wage and price flexibility.

The nature of wage price flexibility is crucial to

our analysis. Assuming that labor is the only variable factor

of production, we define PCM as (P*Q w*L)/P*Q. Where P is

price, Q is output, w is wage rate, and L is employment.

Dividing by Q and simplifying we get PCM = 1 (w/P)*(L/Q).

Let us assume for the moment that L/Q is constant. If wages

are sticky due to contracting and if prices are flexible

then in the short run money growth will increase P, reduce






58

w/P, and increase PCM. If both w and P adjust instantly then

PCM will not change because w/P will stay constant. More

generally, the effect of money growth on PCM will depend on

the relative flexibility of wages and prices and on the

ratio L/Q (what happens to L/Q will depend on the nature of

the production function). If prices are relatively more

flexible than wages then money growth will, in the short run,

increase PCM (assuming L/Q is constant). If we include

materials as a variable factor of production we get PCM = 1 -

(w/P)*(L/Q) (c/P)*(M/Q). Where c is per unit materials

cost and M is total materials used. Here again the results

depend on the relative flexibility of c versus P. If

producers obtain materials via prearranged contracts leading

to sticky short run c, then money growth will increase P and

increase PCM. The net effect will depend on the relative

flexibility of wages, prices, and materials costs. If wages

and/or materials costs are relatively inflexible compared to

prices then money growth will increase PCM in the short run.

From our earlier discussion of price flexibility, and

from the results of Quails, we noted that prices are likely

to be strongly pro-cyclical in more concentrated industries.

In low to moderately concentrated industries, due to lack of

interfirm information flows, prices would tend to be more

stable over business cycles. Linking this up with money

growth, an expansion of money base will lead to a stronger

price response in more concentrated industries. If wages are







59

sticky in the short run and assuming that the degree of

stickiness is even across levels of concentration, growth of

money base will lead to stronger PCM response in more

concentrated industries. In general we should observe a

positive short run relationship between concentration and

the responsiveness of PCM to growth of base money. Since

there are lags between expansion of money base and its

effects on the economy, both current and lags of money growth

will be important in determining current PCM. Finally, if

there is entry in anticipation of demand growth then the

effects on price and PCM will be small. However, if we

preclude entry to short run changes in demand then our above

analysis will go through.

Summarizing, our prediction of the effects of money

growth on PCM is twofold: (i) there is a positive

relationship between money growth and PCM and (ii) the

relationship between money growth and PCM is stronger for

more concentrated industries. In terms of our concentration

classes, we expect the higher classes to show greater

responsiveness to growth of money base. To get evidence on

the above hypothesis we estimate equation 3.4.



PCMC(i)t = UO + alT + a2DQC(i)t + a3DBASEt + a4DBASEt-1

+ Et (3.4)






60

Where PCMC(i) is the mean PCM of the industries in the ith

class. The mean rate of growth of output is denoted by

DQC(i). Growth rate of money base is denoted by DBASE. Data

on base money are from Rush (1986). Industry PCM and demand

growth are as constructed in the previous section. Data on

industry variables were obtained fron the B.I.E. data base.

Since we are interested in analyzing the cyclical nature of

PCM, we include a time trend to capture the deterministic

components.

Regarding sign predictions, we expect both a3 and a4

to be positive. Also, we predict the total effect of growth

of base money on PCM to increase over concentration levels.

The total effect is measured by a3 + a4. We expect this sum

to increase over the concentration classes. From discussions

in the previous section, the sign of a2 is ambiguous. Because

our data base is for a relatively short time period, we did

not experiment with longer lags of DBASE. The results of

estimating equation are presented in table 3.7. The t-

statistics are in parentheses.

First, we note that DASE stands for lagged DBASE. Our

first observation is that all coefficients on the money

growth variables, other than DBASE1 for PCMC1, are positive

as predicted. A majority of them are significant atleast at

the 10% level. Regarding industry demand growth, all

coefficients, other than PCMC1, are positive. They are

significant only for PCMC3, PCMC4, and PCMC7. The time trend






61

coefficients are positive for all and, except for PCMC7,

significant at standard levels. The Durbin_watson statistics

(DW) in general lie in the inconclusive range. Since we



Table 3.7
Cyclical Determinants of PCM


PCMC1 PCMC2 PCMC3 PCMC4 PCMC5 PCMC6 PCMC7

Inter 0.185 0.212 0.222 0.237 0.265 0.256 0.308
(52) (74) (91) (127) (78) (60) (42)

T 0.002 0.001 0.002 0.001 0.002 0.001 0.001
(5.3) (4.6) (9.3) (3.3) (5.2) (2.2) (1.6)

DQ -0.027 0.033 0.054 0.058 0.037 0.023 0.084
(-0.8) (1.4) (2.4) (3.8) (1.3) (0.5) (2.0)

DBASE 0.139 0.093 0.141 0.104 0.202 0.131 0.010
(1.8) (1.6) (2.8) (2.4) (2.7) (1.1) (0.1)

DBASE1 -0.019 0.123 0.144 0.141 0.119 0.223 0.537
(-0.2) (1.9) (3.0) (3.4) (1.6) (2.1) (3.1)


Adj R2 0.846 0.875 0.957 0.901 0.907 0.789 0.711

DW 1.179 1.369 1.440 1.955 1.358 1.215 1.721


cannot decisively reject the null hypothesis of zero

autocorrelation, we do not resort to any mechanical

corrections.

Table 3.8 presents the mean values for price-cost

margins and growth of monetary base. In table 3.9 we present

the sum of the coefficients attached to DBASE and DBASE1.

We denote this by P (p=a3+a4). Table 3.9 also presents the

elasticities (E) between PCM and DBASE, at the mean values.






62

Table 3.8
Mean Values of PCM


Effect of Money


Table 3.9
Growth on PCM by


Concentration Class


The correlation between the 3 coefficients and class

is 0.92 and is significant at the 1% level. The correlation

between the elasticity, E, and class is 0.912 and is

significant at the 1% level. Our analysis provides strong

evidence in favor of the hypothesis that base money growth

and PCM are positively related and that the relationship is

stronger for highly concentrated industries.

Our analysis provides evidence that price-cost

margins are pro-cyclical across all levels of concentration.

Also, more concentrated industries show stronger pro-cyclical

margins. At the lower concentration levels our results are in


Variable Mean Std. Deviation

PCMC1 0.2163 0.018
PCMC2 0.2393 0.015
PCMC3 0.2631 0.021
PCMC4 0.2586 0.011
PCMC5 0.3034 0.021
PCMC6 0.2873 0.017
PCMC7 0.3507 0.026
DBASE 0.0508 0.028


CLASS
C1 C2 C3 C4 C5 C6 C7

S 0.120 0.216 0.285 0.245 0.321 0.354 0.547

E 0.028 0.046 0.055 0.048 0.054 0.063 0.079






63

direct contrast to the results of Domowitz et al. (1986a),

which suggest counter-cyclical margins for less concentrated

industries. Finally, in concluding we note that a larger time

series data base is likely to provide more information on the

relationship between business cycles and price-cost margins.

That would enable us to look at both supply side and demand

side effects and consider longer lags of money growth.









CHAPTER IV
CONCLUSIONS

The role of investment as an entry deterring

instrument has received an increasing amount of theoretical

attention. We set out to analyze the implications of firm

behavior on seller concentration, within the broad framework

of these models. We relaxed a rather stringent assumption

that is common in the existing models, namely that of no

downward adjustment and instantaneous upward adjustment of

capacity. By assuming that firms can adjust capacity both

upwards and downwards, subject to quadratic and symmetric

adjustment costs, we were able to analyze the issues related

to the credibility and desirability of strategic capacity.

From this we derived an inverted U-shaped relationship

between seller concentration and adjustment costs of capital.

Using speed of adjustment as a proxy for cost of adjustment,

our empirical results provided evidence in favor of our

hypothesis. The encouraging nature of the results suggests

useful extension to the 4-digit level.

Our analysis indicates a possible extension. In the

Stackelberg post-entry case, Dixit (1979), Spence (1977), and

Spulber (1981), results show that the incumbent firm

maintains a threat of expanding output in the face of entry.

For this the firm will have to hire labor and incur costs of

adjusting labor. So, in a Stackelberg game one could analyze

64







65

the relationship between labor adjustment costs and strategic

behavior. One could also look at the possibility of firms

holding excess stocks of labor. This would make the analysis

symmetric in both labor and capital.

From the theoretical works it is clear that strategic

investment can be an important form of pre-emptive behavior.

Thus it is necessary to incorporate strategic investment

behavior as an endogenous variable in the study of

industrial structure. One could try to quantify such behavior

by looking at the presence and persistence of idle capacity.

However idle capacity may also exist for other reasons. For

example, firms may invest in capacity in anticipation of

demand growth. In general it would be difficult to separate

out the strategic and non-strategic components of capacity.

Moreover, if firms are playing a Cournot-Nash game, as in

Dixit (1980), then no idle capacity will be held, implying

that we cannot identify strategic capacity by looking at idle

capacity. In such cases pre-emptive investments will be hard

to detect. To the extent that strategic investment can be

quantified, it can be incorporated in the study of industry

characteristics.

Our analysis in section 1 of chapter III shows that

with proper model specification and controlling for cyclical

variability, one can carry out cross section analysis in the

area of concentration-profitability relationship. While

Domowitz et al. (1986a) show that cross section analysis






66

gives us misleading results, they do so by using a

misspecified model. At the 3-digit level, by using averages

of variables over small time periods, and by using a correct

non-linear specification, we show remarkable stability in the

elasticity between concentration and profitability. This

implies that cross section analysis is a legitimate way to

analyse the structure-conduct-performance paradigm.

Lastly we used the growth rate of the monetary base

to examine the cyclical nature of price-cost margins. Our

findings show that margins are strongly pro-cyclical in more

oligopolistic industries.

Regarding implications for antitrust policy, our

analysis in section 1 of chapter 3 is of importance. If,

through proper non-linear model specification, we can

identify a threshold concentration level then this could be

used to formulate antitrust policy. Movements to higher

concentration levels below this threshold level would not

imply significant increase in market power. But a movement to

a concentration level beyond the threshold level would imply

significant increase in market power. Beyond the threshold

concentration level, industries should be subject to scrutiny

for possible entry deterring and other anti-competitive

behavior.










APPENDIX A
DIRECT ESTIMATION OF ADJUSTMENT COSTS

Sargent (1978) outlined a methodology by which we can

directly obtain estimates of adjustment costs of factors of

production. Sargent estimated adjustment costs for straight-

time labor (STL) and overtime labor (OTL) for the American

manufacturing sector as a whole. He found that it was 23

times more expensive to adjust STL as compared to OTL. Also,

Sargent's results showed that the data rejects the rational-

expectations hypothesis. Our main objective is to determine

the viability of this technique for estimating adjustment

costs at a more disaggregated level. Also, it is of interest

here to see whether the rational-expectations hypothesis

finds any support at a more disaggregated level. We reduce

the computational complexity of Sargent's model by assuming

the wage process to be AR(2). Also, we use a more reasonable

estimate of average weekly overtime hours per week.

Sargent's model is a basic labor demand model where

employment depends inversely on real wage. A dynamic

structure is incorporated into the above relationship by

assuming that firms face costs of adjusting labor. So, the

firms find it optimal to take into account expected future

values of the real wage process in determining their current

employment level. Since firms use the moments of the real






68

wage process in their decision making, the rational-

expectations hypothesis is imposed on the model.

A typical firm faces stochastic and quadratic

production functions for STL and OTL as given by equations

A.1 and A.2, respectively. Capital stock is assumed to be a

constant, K.



g1(n1t,K) = (fo+alt)nlt (fi/2)nit (A.1)

g2(n2t,K) = (f+a2t)n2t (fl/2)n2t (A.2)


Where fg, fl > 0 are firm specific parameters. The stochastic

processes alt and a2t affect the productivity of STL and OTL

respectively. The firm can hire STL at real wage w and OTL

at real wage pw, where p (=1.5) is the overtime premium. The

adjustment cost functions for STL and OTL are (d/2)(nlt-

nlt-1)2 and (e/2)(n2t-n2t-1)2, respectively. Where d and e

are the adjustment cost parameters. The stochastic processes

for alt and a2t are:



alt = Plaltl- + elt

a2t = P2a2t-l + e2t



For computational simplicity we assume that the wage process

is AR(2) and is given by:

wt = v0 + vlwt-1 + v2wt-2 + e3t

Given these, the firm at time t chooses nit and n2t to






69

maximize its real present value (PV) given by equation A.3.

PV = Et Zj bJ { (fo+alt+j-wt+j)hlnlt+j (f1/2)hlnlt+j2

(d/2)(nlt+j-nlt+j-l)2 + (fo+a2t+j-1.5wt+j)h2n2t+j

(fl/2)h2n2t+j2 (e/2)(n2t+j-n2t+j-1)2 } (A.3)


Where b (=0.95) is the real discount rate. By assuming the

wage process to be AR(2) we get a three variate vector

autoregression given by the equation system A.4-A.6. (see

Sargent for the complete general specification).


nlt = (81+Pl)nlt-l 61Plnlt-2 + (a2+alvl-alP1)wt-l +

(a i2-a2P1)wt-2 + ult (A.4)


n2t = (0l+Pl)n2t-1 1p2n2t-2 + (P2+P1v1-P1P2)wt-1 +

(P1v2-P2P2)wt-2 + u2t (A.5)


wt = V1lt-1 + v2wt-2 + u3t (A.6)


Since we restricted the wage process to be AR(2), we can

explicitly evaluate the constraints. From the constraints we

get:


61 = ( (37fl/d + 1.95) (37fl/d + 1.95)2 3.61)1/2 )/1.9

01 = ( (7f1/e + 1.95) ((7fl/e + 1.95)2 3.61)1/2 )/1.9

a1 = -61h/d(l 0.95v161 0.90v2612)

a2 = 0.95v261al







70

P1 = -10.501/e(l 0.95v1i1 0.90v2012)

02 = 0.95v2f1 1
We estimate the above model with the method of

maximum likelihood. The equation system has 10 regressors and

7 free parameters (fl, d, e, pl, P2, v1, and v2). We assume

that the error vector is trivariate normal. Following Bard

(1974) and Wilson (1973) we write the logarithm of the

likelihood function as:

Log L(8)= -(l/2)mTLog(2r) (1/2)T{LogjV-hatl+ m)

Where m is the number of variables in the model and T is the

number of observations. Maximum likelihood estimates of the

free parameters are obtained by minimizing IV-hatl (the

determinant of the estimated covariance matrix) with respect

to the set of free parameters.

We first estimate an unconstrained model to obtain

unrestricted log-likelihood, LogL(9)ur. Next, we estimate the

model subject to the constraints (see Sargent for details) to

obtain the restricted log-likelihood, LogL(8)r. We have 10

regressors and 7 free parameters. So -2{LogL(9)r-LogL(e)ur)

is distributed as chi-square with 3 degrees of freedom. High

values of the likelihood ratio lead to the rejection of the

restrictions the theory imposes on the vector autoregression.

Data for estimation were obtained from the Bureau of

Labor Statistics (BLS). Our sample period was from 1965:1 to

1981:2. The series on n1t was obtained from seasonally

adjusted BLS series on employees on non-agricultural payroll.







71

Wage data were obtained from the hourly earnings series. The

nominal wage series was deflated by CPI (1967=100) to obtain

the real wage series. Following Sargent, the series on n2t

was constructed using the following formula:



n2t = ((Ht-hl)/h2)nlt



Where Ht is the seasonally adjusted average weekly hours

series. Average weekly straight-time hours, hl, is estimated

at 37. Sargent assumes that average weekly overtime hours per

week, h2, is 17. Since this estimate seems to be

unreasonable, we use a more reasonable h2=7. All data used

for estimation, n1t, n2t, and wt were detrended by

regressing them on a constant, trend, and trend squared. The

residuals from this regression were used as data. Detrending

is done to isolate the indeterministic components and to

account for changes in the capital stock.

To obtain the estimates of the free parameters of the

model I used the Powell (1968) method of numerical

optimization. This procedure does not rely on computing

derivatives to locate the optimum. Gradient based methods

available in standard econometric packages like SAS and TSP

failed to yield any results, the main problem being related

to convergence. Table A.1 presents the constrained parameter

estimates for the model defined by equations A.4-A.6. Below

the parameter estimates we report the likelihood ratio LR







72

(obtained from the restricted and unrestricted log-

likelihood), the marginal confidence level MCL, and the ratio

of straight-time to overtime adjustment costs d/e.



Table A.1
Industry Estimates


INDUSTRY
Non-Elec. Fab. Pmy. Wood & Elect. &
Parm Machinery Metal Metal Lumber Electronics

fl 0.98 2.74 3.89 4.19 9.54

d 264.5 540.5 180.6 160.9 337.3

e 43.07 153.0 24.14 82.08 168.19

Pl 0.71 0.68 0.41 0.69 0.83

p2 0.21 -0.05 0.37 0.39 0.38

v1 0.86 0.84 0.86 0.93 1.08

V2 -0.17 --0.13 -0.25 -0.18 -0.26

LR 126.8 30.66 1.94 19.76 16.58

MCL 0.99 0.99 0.45 0.99 0.99

d/e 6.14 3.53 7.48 1.96 2.0


From the results we can see that other than Primary

metals, the rational-expectations hypothesis is rejected for

all industries. The high marginal confidence level indicates

that the data contain substantial evidence against the

hypothesis. The ratio of straight-time to overtime adjustment

cost varies between 1.96 for Lumber to 7.48 for Primary

Metals. These estimates are significantly different from






73

Sargent's estimate of 23 for the manufacturing sector as a

whole. Due to the complicated nature of the model we did not

compute any standard errors.

Regarding the use of such estimation procedures for

computing adjustment costs at a more disaggregated level we

note three points:

(1) The optimizing procedures require that we provide

starting values for the parameters. Also, different starting

values are necessary as a check for multiple minima. Our

experiments revealed that the final estimates, specially for

the adjustment cost parameters d and e, were extremely

sensitive to the choice of starting values. Wide fluctuations

in final estimates were observed corresponding to different

starting values. This clearly implies that the parameter

estimates are unreliable.

(2) Numerical optimization procedures are computationally

expensive. The number of iterations to convergence is quite

large. This implies that this procedure cannot be used for a

highly disaggregated study like the one in chapter 2.

(3) Sargent (1978) reports that certain diagonal elements of

the covariance matix of estimates turned out to be negative.

This casts further doubts on the use of such techniques for

estimation.










APPENDIX B
DEMAND UNCERTAINTY AND FACTOR INTENSITY

During the 1970's a series of articles in the

American Economic Review analyzed the influence of demand

uncertainty on firm behavior. Hartman (1976), Holthausen

(1976), Leland (1972), and Sandmo (1971), among others,

looked at the issues related to firm profitability and input

choices under the assumptions of risk-aversion and risk-

neutrality in the presence of demand uncertainty. One of the

results that emerged from this analysis was that risk-averse

firms will use more labor to produce any given level of

output and hence will use a lower capital-labor ratio (K/L).

A risk-neutral firm will operate at the efficient K/L.

However, under the hypothesis of decreasing absolute risk-

aversion, the risk-averse firm's K/L will increase as its

size increases. Therefore the larger the risk averse firm

becomes, the closer it will operate to the efficient K/L.

From this line of reasoning we can see that K/L is

not determined by technology alone, as is commonly assumed.

Assuming risk-aversion, K/L will depend on demand uncertainty

(inversely), firm size (directly), and technology.

Furthermore, from the analysis of Spence (1979) and Eaton and

Lipsey (1979), we can see that firms might install capacity

in anticipation of demand growth. To control for this factor

we need to include demand growth as an explainatory variable.

74







75

We expect K/L to be positively related to demand growth.



K/L = f( DU : SIZE : DG : TECH )
+ +


Where DU is demand uncertainty, SIZE is a measure of firm

size, DG is demand growth, and TECH is technology. Since we

do not have any measure of technology that is independent of

K/L, we drop TECH as an explainatory variable.

We use data on 125 S.I.C. 3-digit manufacturing

industries to test the above relationship. As a measure of

SIZE we use the four-firm concentration ratios (ACR4) from

Weiss and Pascoe (1986). Our sample period is 1958-1977. ACR4

is for the year 1977. The measure of demand uncertainty is

constructed from the following regression:



LogQt = aO + alt + ut



Where LogQ is the logarithm of output and t is time. We use

the standard error of residuals from the above regression as

our measure of demand uncertainty and denote it by DV.

Demand growth is the rate of growth of output over the sample

period. Annual data on gross capital stocks (K), production

workers (L), and output (Q) were obtained from the B.I.E.

data base. K/L is the mean K/L over the period 1958-1977.

Since the theory does not provide us with any functional

form, we estimate a levels equation. Results are reported in







76

the following equation. Heteroskedasticity-consistent t-

statistics are in parentheses.



K/L = 17.06 + 37.63ACR4 74.92DV + 113.73DG
(2.7) (2.4) (-1.88) (1.68)



All variables have the predicted signs and are significant.

To carry out another experiment we use a shorter sample

period, 1973-1977. For this sample we define demand

uncertainty as the coefficient of variation of output (CVQ).

K/L is the mean K/L over the 5-year period and DG is now the

rate of growth of output over the 5-year period.

Concentration measure is for the year 1977. The estimated

equation for this smaller sample period is:



K/L = 11.27 + 59.01ACR4 0.06CVQ + 186.71DG
(1.2) (3.25) (-0.1) (1.6)



As in the previous case we get the expected signs but the

coefficient of variation is insignificant. We must, however,

note that the measure of uncertainty is constructed

differently in the second case. The only consistent result

coming out of the two equations is that K/L and

concentration, and K/L and demand growth are positively

related. Our results suggest a simultaneous determination of

concentration and K/L.










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BIOGRAPHICAL SKETCH

Vivek Ghosal was born in Delhi, India, in 1960. He

received a Bachelor of Arts degree in economics from the

University of Delhi in 1980 and a Master of Arts degree from

the Delhi School of Economics in 1983.









I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.


S ,r '. K. \.-. ,.
A--
Leonard Cheng, Chair -
Associate Professor of
Economics


I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.




Sanford Berg
Professor of Economics


I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.




Roger Blair
Professor of Economics


I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.




John Lynch
Associate Professor of
Marketing







This dissertation was submitted to the Graduate
Faculty of the Department of Economics in the College of
Business Administration and to the Graduate School and was
accepted as partial fulfillment of the requirements for the
degree of Doctor of Philosophy.


August, 1988
Dean, Graduate School




































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