THE EFFECT OF UNIONIZATION ON THE FIRM PROFITABILITY:

THE U.S. MANUFACTURING FIRMS, 1986-1988

by

NEBIYE KARAHASAN, B.A., M.A.

A DISSERTATION

IN

ECONOMICS

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Accepted

Dean of the Graduate School

August, 1993

' ^ \ ACKNOWLEDGMENTS ( J ^ W^/^^

I would like to express my most sincere gratitude to my committee

chairman, Dr. James E. Jonish, for his patience, guidance and support during

my studies. I also wish to thank the other member of my committee. Dr. Lewis

E. Hill, Dr. Sujit Roy, Dr. Klaus Becker and Dr. Terry Von Ende for their

discussions and helpful comments.

I also would like to thank to my husband, Rahmi Yamak, for his valuable

discussions and continuous support. Thanks also to the Government of the

Republic of Turkey for its financial support and also to the Department of

Economics at Texas Tech University for its support during my graduate studies.

Finally, special thanks goes to my parents for their love and continuous

support during my graduate studies.

CONTENTS

ACKNOWLEDGMENTS ii

LIST OF TABLES v

CHAPTER

\. INTRODUCTION 1

II. LITERATURE REVIEW 10

2.1 Industry Level Studies 10 2.2 Firm or Business Level Studies 15

2.3 Summary of Literature 33

III. METHODOLOGY AND DATA 36

3.1 Methodology 36

3.2 Data Description 55

IV. EMPIRICAL RESULTS AND ANALYSIS 63

4.1 The OLS Estimation Results of the PCM, ROI and EV Models on Cross-Section Data, 1988 64

4.1.1 The OLS Estimation Results of the PCM, ROI and EV Models with Firm Union Coverage Ratio 68

4.1.2 The OLS Estimation Results of the PCM, ROI and EV Models with Firm Union Coverage Dummies 73

4.1.3 The OLS Estimation Results of the PCM, ROI and EV Models with Industry Union Coverage Ratio and Dummies 79

4.1.4 Summary Assessment for All Cross-Section Estimation Results, 1988 83

4.2 The Estimation Results of the PCM, ROI and EV Models over the 1986-1988 Period 89

4.2.1 The OLS Results of the PCM, ROI and EV Models over the 1986-1988 Period 90

III

4.2.1.1 The OLS Estimation Results of the PCM, ROI and EV Models with Firm Union Coverage Ratio 92

4.2.1.2 The OLS Estimation Results of the PCM, ROI and EV Models with Firm Union Coverage Dummies 97

4.2.1.3 The OLS Estimation Results of the PCM, ROI and EV Models with Industry Union Coverage Dummies 99

4.2.1.4 Test Results for Sources of Union Gains 103

4.2.1.5 Summary Assessment for the OLS Estimation results over the 1986-1988 Period 108

4.2.2 The Results of the Fixed-Effect Regressions over the 1986-1988 Period 111

4.2.2.1 The Estimation Results of the PCM, ROI and EV Models with Firm Union Coverage Ratio under the Fixed-Effect Technique 113

4.2.2.2 Summary Assessment for the Estimation Results of All PCM, ROI and EV Models under the Fixed-Effect Technique 118

4.2.3 The Results of the Random-Effect Regressions over the 1986-1988 Period 119

4.2.3.1 The Estimation Results of the PCM, ROI and EV Models with Firm Union Coverage Ratio under the Random-Effect Technique 120

4.2.3.2 Summary Assessment for the Random-Effect Regressions 124

V. SUMMARY AND CONCLUSION 126

5.1 Summary of Results 128

5.2 Conclusion 130

REFERENCES 135

APPENDIX 143

IV

LIST OF TABLES

3.1 Variable Definitions and Sources

3.2 Descriptive Statistics of the Variables

4.1 OLS Resu

4.2 OLS Resu

4.3 OLS Resu

4.4 OLS Resu

4.5 OLS Resu

4.6 OLS Resu

4.7 OLS Resu

4.8 OLS Resu

ts of PCM Model with Firm Unionization, 1988

ts of ROI Model with Firm Unionization, 1988

ts of EV Model with Firm Unionization, 1988

ts of PCM Model with Industry Unionization, 1988

ts of ROI Model with Industry Unionization, 1988

4.9 OLS Results of EV Model with Industry Unionization, 1988

4.10 OLS Results of PCM Model with Industry Unionization Dummies, 1988

4.11 OLS Results of ROI Model with Industry Unionization Dummies, 1988

4.12 OLS Results of EV Model with Industry Unionization Dummies, 1988

4.13 OLS Results of PCM Model with Firm Unionization, 1986-88

4.14 OLS Results of ROI Model with Firm Unionization, 1986-88

4.15 OLS Results of EV Model with Firm Unionization, 1986-88

4.16 OLS Results of PCM Model with Firm Unionization Dummies, 1986-88

59

61

69

72

74

ts of PCM Model with Firm Unionization Dummies, 1988 75

ts of ROI Model with Firm Unionization Dummies, 1988 77

ts of EV Model with Firm Unionization Dummies, 1988 78

80

81

82

84

85

86

93

95

96

98

4.17 OLS Results of ROI Model with Firm Unionization Dummies, 1986-88 100

4.18 OLS Results of EV Model with Firm Unionization Dummies, 1986-88 101

4.19 OLS Results of PCM Model with Industry Unionization Dummies, 1986-88 102

4.20 OLS Results of ROI Model with Industry Unionization Dummies, 1986-88 104

4.21 OLS Results of EV Model with Industry Unionization Dummies, 1986-88 105

4.22 Test Results for Source of Union Gains for PCM, ROI and EV Models, 1986-88 106

4.23 Fixed-Effect Results of PCM Model with Firm Unionization, 1986-88 114

4.24 Fixed-Effect Results of ROI Model with Firm Unionization, 1986-88 115

4.25 Fixed-Effect Results of EV Model with Firm Unionization, 1986-88 116

4.26 Random-Effect Results of PCM Model with Firm Unionization, 1986-88 121

4.27 Random-Effect Results of ROI Model with Firm Unionization, 1986-88 122

4.28 Random-Effect Results of EV Model with Firm Unionization, 1986-88 123

A.I Names and Industry Codes of the Firms 143

VI

CHAPTER I

INTRODUCTION

Since 1970s, the beginning of substantial decline of unionization in the

manufacturing sector of the United States, the literature on labor and industrial

economics has devoted much attention to investigating the hypothesized

relationship between the unionization and profitability.

The relationship between unionism and profitability has been a locus

point of the empirical literature since the studies of Freeman (1983) and Clark

(1984), although a particular relationship between unionization and profitability

has been discussed theoretically for a long time. Both studies are influential in

this specific area because the effect of unionism on profitability is being the first

time, empirically investigated by these studies. Until these studies, of course, a

considerable body of literature concerned with the empirical determinants of

profitability exist. Unfortunately, no studies did "explicitly" concentrate on the

relationship between unionism and profitability. For example, no study which

includes unionism, as well as other explanatory variables, on the profitability

equation exists among the forty-six profit studies surveyed by Weiss (1974).

Formally, the first empirical attempt in testing the effect of unionism on

profitability comes from Freeman (1983). By using a sample of industry-level

data for the period 1958-1976, Freeman (1983) scrutinized what the effects of

2

unionism on industry profitability measures, such as price-cost margin and the

ratio of quasi-rents to capital, were. His conclusion was that, regarding both

profitability measures, there was a negative and substantial effect of

unionization on industry profitability. This conclusion was later supported by the

study of Clark (1984), using a sample of North American product-line business

data and rate of returns on sales and capital, separately, as a measure of

profitability. After these pioneering studies, a body of studies exist which deal

with the same hypothesis under different sample of data and different measures

of both unionization and profits. The studies of Hirsch (1991a; 1991b), Becker

and Olson (1992), Bronars and Deere (1990) and Abowd (1989), among others,

are examples of the recent ones.

In general, the effect of unionism on profitability differs, depending on the

sample of data, the measure of profitability, the measure of unionization and the

estimation technique used although there has been an agreement on the

negative union effect on profitability. Therefore, the dispute pertaining to this

literature is ongoing as to the appropriateness of profit measure, union measure

and the estimation technique.

At this point, it is appropriate to categorize the studies into two general

groups in terms of the sample of data: industry level studies and firm level

studies. To categorize studies in this manner is important because the

profitability measure that is chosen largely depends on the availability of

information on the data. The most common measures used in industry level

studies are price-cost margin and rate of return to capital (accounting profit

measures) while in firm level studies, they are Tobin's q and excess value

(market measure) in addition to accounting profit measures. Unfortunately,

none of these measures is theoretically and empirically found to be entirely

unambiguous and satisfactory. Both advantages and disadvantages of each of

these measures have been extensively discussed in the previous studies.

Using a sample of aggregate manufacturing data for 1972, Karier (1985)

estimated that the union captured about 68 percent of the price-cost margin

while, with a sample of firm level data for 1977, Becker and Olson (1992)

concluded that the price cost margin for a fully unionized firm was lower by 30

percent. In comparing the impact of unionization on both accounting profit and

market value measures, Hirsch (1991b, p. 11) made the argument that

...these effects on market value can differ from unionism's impact on current earnings. For example, a union may significantly decrease current earnings but not market value if investors believe the firm can adjust in the future or in some way offset the union's current negative impact. Or, a union may have little immediate impact on earnings but significantly decrease market value if investors expect the union to have a detrimental effect on firm growth and future earnings.

Using firm-level data, in another study, Hirsch (1991a) found that while Tobin's

q (market value measure) for an average unionized firm was lower by an

average 12.4 percent, the rate of return on capital (accounting measure of

earnings) was lower by 9.2 percent, which supported his argument above.

The eariy studies, generally and necessarily, have used industry

unionization as a proxy for firm unionization measure because of the

unavailability of data on firm-level unionization, while most of their data, except

unionization, came from firm level. The studies of Salinger (1984), Connolly et

al. (1986) and Hirsch and Connolly (1987) are examples for those of the group.

The common conclusion of most of those studies was that the effect of

unionism on profitability was negative and statistically significant. As more data

on the firm level became available, recent studies started to devote more

attention to re-investigating the relationship between unionization and

profitability by using more rigorous data on unionization. The studies, which

compared the results, coming from the industry level unionization with those

obtained from the firm-level unionization, argued that to use the firm union

measure as a proxy of unionization in micro level data provided better results

than using the Industry union measure. For example, Hirsch (1991a) and

Becker and Olson (1992) found that the effect of firm union measure on

profitability was always negative and significant while the impact of industry

union measure on profitability was mostly insignificant. Then, in this respect,

Hirsch (1991a) made a conclusion that firm level union measures were

preferable to Industry union measures since industry union measures suffered

from the intra-industry variability.

More Importantly, the conclusion obtained by the previous studies on the

magnitude of the effect of union on profitability appeared to be sensitive, not

5

only to the level of data, but also to alternative measures of the percentage

unionized which enriched the understanding of unionization impact even though

the same spline function was used. In their studies, Hirsch (1991a) and Becker

and Olson (1992) separately used dummy variables to capture the

union-nonunion distinction, rather than basing them solitarily on the variation in

the percentage unionized. By replacing firm unionization with the same spline

function, the findings of Hirsch revealed that a significant portion of union gain

rises across 30 and over percent of unionization for the market value equation,

and over all the range of percentage unionized for the accounting profit

equation. But, later Becker and Olson (1992) found that this occurred over the

first 30 percent of unionization for both profit equations.

The most important one of the limitations in most of the previous

empirical studies on unions and firm profitability has been the difficulty in

obtaining firm-level measures of union coverage. Although recent studies could

obtain firm-level union measure, the data on this variable came from only one

specific year. Therefore, these studies were forced to combine the information

on firm union coverage or union membership which came from only one

specific year with other information related to the firm performance which came

^Some previous studies defended that use of dummy variable which is alternative to percentage unionization has more empirical advantage to alternative one. For example, Clark (1984) made an argument that because of the possibility on the inaccuracy of union data collected use of dummy variable is more realistic to accurate identify whether a business is unionized or nonunionized.

6

from more than one year. Also, this limitation on availability of union coverage

data over time forced the previous studies to use certain kind of estimation

technique. Obviously, since the previous studies could not specify their profit

equation so as to capture the possible effects of omitted or mismeasured firm

specific, industry specific and/or economy wide variables which exhibited

variation across firms and/or through time, and also were correlated with firm

unionization and profitability, the estimated union effects on profitability would

be biased if this was the case. Then, the conclusion on the magnitude of the

union effect on profitability would be misleading. In practice, one feasible

technique to rule out the possibility that the parameter estimates suffer from

unobserved or mismeasured determinants of profit is to use the fixed effect

under panel data. For example, Hirsch in 1991 (1991a, p. 74) argues that

...despite the inclusion of detailed control variables in our regressions, there may be omitted determinants of profitability correlated with the union coverage variable. A potential method to account for omitted variable bias is the use of a fixed-effect or difference model, wherein changes in profitability are estimated as a function of changes in union coverage. But such estimation is not possible here, since union coverage is measured only for 1977.

Also, In another study, Hirsch (1991b, pp. 122-123) in 1991 indicates that

...further study of the relationship of union coverage with economic performance is needed. ...analysis of the performance of union an nonunion companies during a more current period is essential. It is certainly possible that negative union effects

For example, in his recent studies, Hirsch (1991a) combined the sample of one year (1977) on union variable with the sample of more than one year (1972-1980).

on firm performance have been partially mitigated in the 1980s, owing the management and union response both to the forces of domestic and foreign competition and to the poor performance outcomes In the past.

The main objective of this study is to analyze statistically and

comprehensively the effect of unionism on firm profitability and the sensitivity of

such effect to different measures of firm profitability and unionization, and

different estimation techniques.

The specific objectives of the study are:

(1) To analyze the impact of firm unionization on firm profitability

regarding the alternative econometric techniques; that is, ordinary

least squares versus fixed-effect technique.

(2) To examine the impact of firm unionization on firm profitability

regarding the alternative definitions of profit; that is, accounting profit

measures versus market value measures.

(3) To examine the impact of firm unionization on firm profitability

regarding the alternative measures of unions; that is, union coverage

versus union dummies.

(4) To examine the impact of an alternative level of aggregation of union

variable on union-profit effect; that is, firm union variable versus

industry union variable.

The secondary objective of this study is to investigate whether the

returns to research and development investment are still major source of union

8

gains, even though the investment on research and development falls in recent

years.

The basic analytical tools being used to carry out the stated objectives

are ordinary least squares, fixed effect and random effect techniques. In testing

for heteroscedasticity under ordinary least squares technique, both the

Goldfeld-Quandt test (1965) and the White test (1980) are used. In the

presence of heteroscedastic error terms, the White correction procedure (1980)

is employed to correct the residual terms. In addition, the investigation for any

endogeneity between the unionization and profitability is carried out using the

Hausman exogeneity test (1978).

At this point, before starting the subsequent chapter, it is appropriate to

set out the overall plan of this study. The present chapter provides general

information on the statement of the problem, the basic objectives of the study,

and the general analytical tools and methodology used to carry out these

objectives. Chapter II presents a selective review of the literature related to the

effects of unions on profitability. The focus of Chapter II is on the measures of

the profitability and unionism constructed and on the empirical techniques used

in testing the effect of unionism on profitability. Chapter III discusses the

empirical procedures, the analytical tools, the data sets used and the variables

constructed from them. Chapter IV covers the empirical results of this study, an

analysis of the studies, by comparing them with each other and also with

previous studies. Finally, Chapter V provides a summary of the empirical

results, an assertion of the contribution of this study to the existing literature,

the major limitations of this study and the areas suggested for further research.

CHAPTER II

LITERATURE REVIEW

The purpose of Chapter II is to present a selective review of literature

related to the relationship between the profitability and unionization. This

chapter covers the literature from the influential studies of Freeman (1983) and

Clark (1984) to recent studies.•• Since the sample data and the constructed

profitability measures are generally in two forms, the review of literature is

accomplished by dividing the studies into two groups: industry level studies and

firm level studies.

2.1 Industry Level Studies

Since among the industry level studies. Freeman's (1983) was the first to

attempt to investigate the relationship between profitability and unionization

directly, it is appropriate to start the review of literature with his study.

As previously noted, the studies of Freeman (1983) and Clark (1984) are the first to investigate the relationship between the profitability and unionization directly. Undoubtedly, prior to these studies, there is an explosion of research concerning the relationship between the unionization and economic performance such as productivity. But, in these studies, the relationship between the unionization and profitability is examined indirectly. For example, the studies of Brown and Medoff (1978), and Clark (1980) suggest that unionization does not increase production costs and then that unionization does not reduce profits, rather may increase profits.

10

11

By constructing two separate samples of 139 three-digit industries from

Annual Survey of Manufactures, ASM,: 1958-1976 and of 68 three-digit

industries from Internal Revenue Service, IRS,: 1965-1976, Freeman examined

the possible effect of unionism on the industry profitability which was measured

by price-cost margin, PCM, and the ratio of quasi-rents to capital. Under the

ordinary least squares technique, he regressed both profit measures on the

union variable, the ratio of capital to business receipts and other market

structure variables such as industry concentration, advertising intensity,

absolute capital requirements and relative minimum effect scale. According to

his coefficient estimates which were evaluated at the mean of dependent

variable, for the IRS sample, union lowered the rate of return to capital by some

61 percent for a fully unionized manufacturing industry. Regarding the PCM

measure, his results were similar to the rate of return measure; negative and

significant union effect on profitability. Although his findings from the IRS data

set were consistent with those from ASM data set, the size of the effect

obtained from both data sets differed significantly. When union coverage

moved from 0 to 100 percent, both measures for the industry profitability fell

between 13 and 44 percent for the IRS sample while they fell between 13 and

19 percent for the ASM sample. In his same study. Freeman additionally

investigated how the effects of union behaved in concentrated and

In literature, whether advertising is treated as a mean to entry or to create a barrier to entry has been in dispute. For this issue, see Mann (1974), Brozen (1974), Martin (1979), Strickland and Weiss (1976), and Gisser (1991).

12

unconcentrated industries. His findings revealed that the negative effect of

unionism on industry profitability was limited to highly concentrated industries.

In this respect, he concluded that unionism had essentially no impact on

profitability in more competitive industries but a sizeable negative effect in the

concentrated industries.

Later, Karier (1985) extended the study of Freeman to a sample of data

from the 1972 Census of Manufactures and Annual Survey of Manufactures.

For this purpose, he used only the PCM measure for the profitability and

included a set of regional dummies in addition to the explanatory variables of

Freeman into his specification. His ordinary least squares results were in

complete agreement with the findings of Freeman, that is, unions significantly

lowered the PCM. His finding revealed that union captures about 68 percent of

potential monopoly profit. Karier also investigated how this effect behaved in

highly, moderately and lowly concentrated industries. His findings showed that

unions had a negative and significant Impact on PCM in highly and moderately

concentrated industries yet they had little effect on PCM when markets were

more competitive which was also consistent with Freeman's conclusion.

More recently, in another study, Karier (1988) enlarged his previous

study by focusing more on the source of union rent sharing behavior and

therefore on interaction terms of concentration-unionization as well as

concentration-imports. With a sample of 107 three-digit manufacturing

industries over the 1965-1980 period from the same data sources as described

13

above, his generalized least squares results indicated that in addition to unions,

imports also reduced total monopoly profits. His results also revealed that total

monopoly profits were divided among imports (14 percent), unions (47 percent)

and firms (39 percent).

Until 1986, the issue on the endogeneity of unionization with respect to

profitability was ignored in virtually all empirical studies which investigated the

relationship between industry unionization and profitability. Voos and Mishel

(1986a) were the first not only to make the argument that the effect of

unionization on industry profitability was underestimated in the standard single

equation technique if unions were more likely to organize in the most profitable

industries but also to consider the endogeneity of unionization with respect to

profitability in their estimation procedure.

By constructing a sample of 139 three-digit manufacturing industries from

the Annual Survey of Manufactures and Census of Manufactures for the period

of 1968-1972, Voos and Mishel estimated a two-equation model, wherein

unionization was treated as endogenous with respect to profitability and a single

equation model, wherein unionization is assumed to be exogenous. In both the

single equation model and the first equation of a two-equation model, they

regressed the price-cost margin measure on industry union coverage and

market structure characteristics (concentration ratio, measure of capital

intensity, advertising intensity, import penetration, barriers to entry, size of

establishments and sales growth). In the second equation of their two-equation

14

model, they regressed logistic transformation of union coverage on average

characteristics of the workers employed in the Industry (race, sex, education,

experience, types of job), characteristics of industry (concentration ratio,

capital-to-labor ratio, injury rate) that affected the level of unionization and PCM.

According to their OLS estimates which were evaluated at the mean of

dependent variable, a fully unionized industry had a 22.6 percent lower PCM

than a nonunionized industry. On the other hand, their two-stage estimates of

the union impact on profits suggested that union reduced PCM by 35 percent in

the industries that they organized. Since two-stage estimates were found to be

significantly larger than OLS estimates, they concluded that endogeneity was

an important consideration in estimating the union effect on profits.

On the other hand, Domowitz, Hubbard and Petersen (1986) argued that

cross-sectional estimates of the "union-profits" effect were likely to be biased

because of the exclusion of the demand effect in estimating the impact of

unionization on the profitability. Therefore, in addition to industry union

measure and industry market structure variables (concentration rate, the ratio of

capital to output, the ratio of advertising to sales), they also included

economy-wide unemployment rate and some interaction terms between

unemployment rate and rest of the variables to capture the aggregate demand

effect in their OLS estimations. For a sample of 284 four-digit manufacturing

industries from the Census of Manufactures over the 1958-1981 period, their

results indicated that on the average PCM was reduced by about 25 percent if

15

union coverage moved from 0 percent to 100 percent. Moreover, they found

little evidence to substantiate the claim that the negative effect of unionism on

industry profitability existed necessarily in highly concentrated industries.

According to their OLS estimates, both unionized and concentrated industries

had higher PCM than the average PCM during periods of low unemployment

whereas they had lower PCM than the average PCM usually experienced

during periods of high unemployment. Therefore, their conclusion followed that

the effect of unionization on PCM depended on the state of demand.

Recently, in another study, Domowitz et al. (1988) re-examined the

relationship between markup of price over marginal cost and unionization by

using the same sample of data over the 1958-1981 period. They estimated the

markup of price over marginal cost as a function of import-adjusted

concentration ratio and union coverage ratio. Their estimation results provided

evidence that markup was reduced by about 17 percent when union coverage

was equal to 100 percent relative to when it was zero.

2.2 Firm or Business Level Studies

In addition to the industry level studies, there have been a body of

studies concerning the relationship between unionization and accounting profit

measures (and/or market value measures) in the micro level data such as firm

or business level data.

16

Among these studies, Clark's (1984) was the first to examine the

relationship between unionization and performance (profitability, growth of sales

and productivity) in a sample of 900 North American product-line businesses

which were classified as large manufacturing firms and participated in the PIMS

project from 1970 to 1980. In a part of his study related to the relationship

between unionization and profitability, he utilized the rate of returns on sales

and capital, separately, as a function of unionization measures, firm control

variables, industry market structure variables and industry labor market

variables. His OLS results confirmed that the rate of return on sales and on

capital in a fully unionized business was about 18 percent lower than that in

nonunionized business. Moreover, when dummy variables which were

constructed from percentage unionization were used instead of percentage

unionization for a line of businesses, his spline function suggested that a

significant and negative union effect for the businesses wherein unionization

ranged from 0 to 30 percent and no significant union effect for the rest of the

businesses. In order to see whether a firm's market share provided more likely

sources for union rents, Clark re-estimated the same regressions for both

low-market share firms and high-market share firms separately. In contrast to

Freeman (1983) and other previous studies, Clark's results revealed that unions

decreased profits only among the firms which had small market shares (here

firms with less than a 10 percent share). His findings showed that for

low-market share firms, there was about a 40 percent decline in rate of return

17

on both capital and sales due to unionization while for the high-market share

firms, the effect of unionization on profitability measures was statistically

insignificant.

Like Clark (1984), in 1984 Salinger investigated the relationships among

unionization (here, industry union coverage was proxied for firm union

coverage), market structure and profitability measures such as Tobin's q and

rate of return on capital by using large manufacturing firm data from 1979

Compustat tape and PICA data set. First, he argued that Tobin's q was a

better measure of monopoly profits than other single period profit measures

because it measures the long-run monopoly power.^ Then, estimating a

nonlinear least squares profit equation for Tobin's q and rate of return on

capital, he found that the unionization had a negative and significant effect on

Tobin's q while it had insignificant impact on rate of return. His findings showed

that unions capture 11 percent of the monopoly rents (for Tobin's q). In

addition, he found that the negative effects of unionization were limited to highly

^On the other hand. Shepherd (1986) argued that Tobin's q as a measure of firm profitability was more likely to be inferior to other profit measures because: (1) Tobin's q ratio was an event of capital markets, not of the firm itself. (2) Tobin's q ratio was complicated to measure since the market value of the firm, which is in the numerator of the Tobin's q ratio, was a proxy book value, and its accuracy was questionable, and hybrid book-and-replacement value figure, which is in the denominator of Tobin's q ratio, included unknown degrees of error and was likely biased.

18

concentrated industries and that both concentration and entry barriers made it

easier for unions to raise wages."*

Later, Salinger's results for firm level data were also supported by the

study of Voos and Mishel (1986b), by using a sample of data from the U.S.

Supermarket Chains for the 1970-1974 period. The purpose of Voos and

Mishel's study was to analyze whether unionization reduced accounting profits

for large companies in the supermarket industry and whether its impact was

larger when local markets were more concentrated. For this purpose, the ratio

of firm profits to sales was regressed on the market structure and firm

characteristics variables. According to their generalized least squares

estimates, unionization reduced the ratio of firm profits to sales by 76 percent

on average in the supermarket firms in standard-metropolitan area markets but

the reduction in profits for firms in concentrated industries was larger than that

in unconcentrated industries.

In addition, Connolly, Hirsch and Hirschey (1986) scrutinized the

relationship between unionization and research and development expenditure

as well as the association between unionization and market value of firm (here

excess value) by using the same proxy for unionization as in the study of

" For a review of the literature on the relationship between profitability and market concentration rate and/or market share, see Gale (1972), Demsetz (1973, 1976), Weiss (1974), Cowling and Waterson (1976), Phillips (1976), Bond and Greenberg (1976), Carter (1978), Porter (1979), Ravenscraft (1983), Smirlock, Gilligan and Marshall (1984), Schmalensee (1985), Harris (1986), Shepherd (1986), Scott and Pascoe (1986), Uri (1988) and Amato and Wilder (1990).

19

Salinger (1984). For a sample of 367 firms from the Fortune 500 for 1977,

Connolly et al. (1986) specified one equation for each relationship. In the first

equation, excess value, which was a measure of firm profitability, was specified

to be a function of unionization, intangible capital, output market structure and

control variables. On the other hand, in the second equation, research and

development expenditures as a measure of intangible capital were set to be a

function of unionization, excess value and other control variables. Whereas no

evidence on misspecification of excess value equation was found from

estimating it with the ordinary least squares technique, clear evidence on

misspecification of research and development equation was found as a result of

using ordinary least squares technique. Therefore, they used two-stage least

squares technique to capture the effect of endogenous unionization measure

with respect to research and development expenditure. According to ordinary

least squares estimates from the first equation, unions had a negative and

significant effect on firm excess value. From the second equation, both the

ordinary least squares and two stage least squares results revealed that the

impact of unionization on research and development expenditures was also

negative and significant. In contrast to Freeman (1983), Karier (1985) and

Salinger (1984), Connolly et al. (1986) could not find any strong evidence that

union captured rents associated with the output market structure (concentration

ratio, market share). In other words, their estimates rejected the proposition

that market share and concentration were a significant source of union gains.

20

In another study, Hirsch and Connolly (1987) extended the study of

Connolly et al. (1986) to a sample of 367 firms data from Fortune 500 firms in

1977 in order to re-examine the relationship between unionization and firm

profitability. By using Tobin's q and the rate of return on sales as the firm

profitability measure, ordinary and two-stage least squares results provided

evidence that unionization decreased firm profits, but this negative effect was

sensitive to model specification. For a fully unionized firm, union reduced

Tobin's q by 13-20 percent while it reduced the rate of return on sales from 11

to 17 percent. In addition, their estimates indicated that unions captured

monopoly profits associated with firm's expenditure on research and

development, its protection from foreign competition and its market share, that

did not associated with concentration. Obviously, this finding was in sharp

contrast with the conclusion of Freeman (1983), Karier (1985) and Salinger

(1984) whose results indicated that unions captured monopoly profits

associated with industry concentration.

Until Hirsch's study (1990), in virtually all firm level studies, firm

unionization was proxied to industry union coverage because firm level

unionization measure was not explicitly and publicly available. Hirsch was the

first to utilize firm specific union coverage from his own recent survey. By using

his survey data on firm union coverage, market share and concentration, Hirsch

(1990) Investigated the impact of unionization on firm profitability, and tested

whether market power was a primary source of union gains. For this purpose.

21

he matched the 1977 union coverage variable to the other determinants of profit

from different sources and different years over the 1972-80 period. For the

estimation, he used a two-step model because the error term was serially

correlated across the years within firms. First, firm profitability measures such

as the natural log of Tobin's q and the rate of return on capital were regressed

on 246 manufacturing firm dummies and all time varying profit determinants

such as research and development intensity, advertising intensity, firm size,

capital intensity, firm specific sales growth and foreign competition. In the

second step, the coefficients of the firm dummies were first regressed on

time-invariant variables such as firm-specific union coverage, sales weighted

four-firm concentration ratio across industries, firm's sales-weighted market

share across industries and industry union coverage in the firm's principal

two-or three-digit industry. Then, the regression equations were weighted by

the inverse of the standard errors of the firm dummy coefficients. The

estimated union impact on firm profitability in this study was consistent with the

previous estimates. For 247 large manufacturing companies over the period of

1972-1980, he reported that a change from 0 percent to 50 percent unionization

implied a decrease in Tobin's q by 9.0 percent and in the rate of return on

capital by 6.9 percent. However, he found no evidence to support the

hypothesis that industry concentration and firm's market share were major

sources of union gains, but confirmed that the major sources of union gains

were the returns of research and development investment. Therefore, his result

22

was also more likely to support the finding of Clark (1984) which stated that

unions only decrease profits of businesses with small market shares.

In more recent studies, Hirsch (1991a, 1991b) examined the relationship

between firm profitability and unionization by using a firm level coverage

measure for a larger sample of manufacturing firms relative to that in previous

studies. In both studies, he combined various data for a sample of 705

manufacturing firms over 1972-80 period from R&D Master File, Compustat, his

union survey, Word's Business Directory and the Annual Survey of

Manufactures. For the statistical analysis, he used the same estimation method

as in his previous study, a two-step model. By using union coverage at the firm

level, he found a strong negative relationship between unionization and Tobin's

q as well as between the rate of return on capital and unionization, and also

confirmed that union effects on profitability still remained important over the

1972-80 period. His findings revealed that for an average unionized firm,

Tobin's q and rate of return to capital were lower by 12.4 percent and 9.2

percent, respectively. On the other hand, when he used industry union

coverage as a proxy for firm unionization, he found no significant union effect

on firm profitability. Furthermore, by using union coverage dummies

constructed from union coverage ratio, Hirsch found that unionization had

significant impact on the rate of return on capital in all ranges of unionized

percentage, whereas, a significant part of union gains existed across 30 and

over percent of unionization in the Tobin's q equation.

23

In 1992, Becker and Olson (1992) extended the literature on how

unionization affected firm profitability and what the source of union rent sharing

behavior was, by employing a different proxy for firm unionization measure than

used by Hirsch (1991a, 1991b). The firm unionization measure was the ratio of

pension plan enrollment through collective bargaining divided by the total firm

pension plan enrollment, not directly the ratio of unionized employees to the

total firm employment. However, they used not only firm level but also industry

level unionization measures in their estimation. Their main data sources on a

sample of about 300 U.S firms were the 1977 Annual Return-Report of

Employee Benefit Plan (form 5500) and the Compustat data set. As a proxy for

firm profitability, excess value (market value), which was produced by the

difference between the capital market valuation of firm assets, and rate of

return on sales (accounting profit measure) were employed.

In the first part of their study, Becker and Olson searched for the answer

as to how industry and firm unionization measures, market concentration,

intangible assets and other control variables affected the excess value of firm

and the rate of return on sales under the ordinary least squares technique.

According to their parameter estimates, only the firm unionization measure was

associated with a substantially lower rate of return on sales and excess value,

although both firm unionization and industry unionization had a negative effect

on both measures of firm profitability. More specifically, their coefficient

estimates suggested that a fully unionized firm had an 18 percent lower excess

24

value below the mean than that of a nonunionized firm while at the mean of the

dependent variable, a 100 percent unionized firm had a 30 percent lower rate of

return on sales than that of a nonunionized firm. When they investigated the

possible variations in the union effect on firm profitability over the range of

unionization, they found that a negative and significant effect existed for the first

30 percent of unionization. Finally, from the estimations of their profit equations

with the interaction terms between unionization and research and development

expenditure, they concluded that research and development expenditure was

an Important source of union gains in the excess value equation, though

significantly less so in the rate of return equation.

In the literature, there have been some studies which dealt with the

profitability-unionization relationship for the manufacturing firms of countries

other than the U.S. manufacturing firms. Among the recent ones, using the

establishment level data from the 1980 and 1984 Workplace Industrial Relations

Surveys which covers 2040 and 2019 British establishments with 25 or more

employees, respectively, Machin and Steward (1990) estimated the effect of

unionization on financial performance of establishments under statistical method

and financial performance measures different from the previous studies. In

estimating the specified performance equation, they used "the ordered probit

estimate method" since their financial performance measure was qualitative

such as below, above or about industry average of financial performance. They

regressed this qualitative dependent variable on establishment-level variables

25

(unionization, market share, capital intensity and growth in the demand

variables) and on industry level variables (five firm concentration ratios, industry

level price-cost margin variables). Although the profitability measure and the

estimation method were quite different from the standard approach, their

findings supported the previous results of U.S. firm level studies: a negative and

significant union effect on firm performance. More specifically, unionized

establishments were more likely to have about average and especially, below

average levels of industry performance. Their results also indicated that unions

significantly restricted financial performance for more likely establishments

which had a larger market share in terms of employment.

In another study, Machin (1991) used a sample of 290 large British

manufacturing firms from the Data Stream and Exstat data bank. Census of

Production and Survey of Companies for 1984 and 1985 in order to investigate

the relationship between unionization and firm profitability. His study was the

first to employ a sample of British firms based on the actual profit data.

The ratio of accounting profit to sales was used as a proxy for the firm profit

measure while dummy variables were constructed for the firm unionization

measure. In addition to firm and industry unionization measures, he employed

market share (in terms of firm sales), industry concentration ratio, and some of

the firm level variables as independent variables. The coefficient on the

recognition dummy for firm unionization was estimated to be negative and

statistically significant, suggesting that unions reduced the rate of return on

26

sales by some 2.3 percentage points. His OLS results also confirmed that the

ability of unions to capture monopoly rents was not related to concentration, but

it was related to the firm specific market share (firms which have a high market

share) and to the degree of union activity in the firm's operating industry

(industries which are highly unionized).

In the case of Japanese manufacturing firms, more recently, Brunello

(1992) analyzed the relationship between unionization and firm performance

(profit, wage and productivity) for 979 unionized and nonunionized

manufacturing firms which were drawn from the 1987 issue of the Yearbook of

Japanese Unlisted Companies. In his profitability equation, the rate of return on

invested capital and the profits-to-sales ratio were set to be a function of

technological change measures (capital-to-sale ratio), market structure

measures (market share of the firms), labor quality measures (average age of

employees, the ratio of female to male employees), firm union measure which

took the value of 1 if the firm is unionized, 0 if not unionized, and some

interaction terms.

By using both OLS technique and the combination of Heckman's

two-step procedure with instrumental variables for factor inputs, IVM, Brunello

found similar results to most of U.S. firm level studies: the effect of unions on

firm profitability was substantial and negative. However, if the dependent

variable was the rate of return on invested capital, the unionization effect on

profitability was -19.56 percent with OLS estimates and -23.80 percent with

27

IVM. On the other hand, if the dependent variable was the profits-to-sales ratio,

the effect of unions on profitability was -36.5 percent with OLS estimates and

-37.3 percent with IVM estimates. Finally, his results revealed that the union

effect on firm performance was substantially smaller in small-and medium-size

firms than that in large firms.

Although in the literature, the profitability measures, which were

constructed and widely used by most of the previous studies, are the price-cost

margin, the rate of return on capital, and the market value of firm, there have

been some studies which used completely different firm profitability proxies (for

example, equity value of the firm and unexpected changes in shareholder

wealth) as well as different unionization measures (petition date, certification

date and unexpected changes in unionized workers wages).

For example, in 1984, Ruback and Zimmerman (1984) investigated the

relationship between unionization and equity value of the firm (abnormal

monthly common stock returns) in the respect of union representation election.

In other words, they analyzed how petitions and certification of election affected

the stock prices of firms. The expected return of a firm's securities, which was

the difference between the actual market return of securities and the estimated

market return of securities was proxied to the abnormal common stock returns

of firms. With a sample of 253 manufacturing firms in the New York Stock

Exchange over the period 1961-80, they regressed the abnormal returns on a

series of binary variables representing petition date, election date, different

28

unions and different industries. For the estimation of the effect of different

unions and industries on the equity value of firms, they used weighted least

squares in order to correct for heteroscedasticity. The evidence presented in

their study suggested that union representation election tremendously lowered

the equity value of firms; that is, stock prices fell by 1.38 percent when unions

petitioned for representation elections. They also found that the combined

abnormal return on the petition and certification month fell 3.8 percent when the

union won the election and 1.3 percent when the union lost the election.

Recently, by analyzing which firms experienced the largest declines in

firm's equity value because of new union representation elections and which

factors helped to incur these large equity losses resulting from those elections,

Bronars and Deere (1990) extended the study of Ruback and Zimmerman

(1984). Bronars and Deere used the same data source as did Ruback et al.

(1984), but used a different sample (firms in all sectors except service sector)

and event. By using a sample of unionization elections, which were limited to

those occurring in firms in the New York Stock Exchange firms between

February 1962 and September 1980, from National Labor Relations Board

Files, they picked up the sample of 255 elections at 137 different firms. Two

different dependent variables, the percentage change in the firm's equity value

in certification month and the percentage change in the firm's equity value in

petition month are set to be a function of a vector of observable characteristics

of firm and industry. According to their weighted least squares estimates, union

29

election events had a negative and significant impact on the firm profitability

only during the month in which the petition for an election was filed but, the

effects during the certification month were much weaker than the petition month

as Ruback and Zimmerman indicated. In addition, they found that equity losses

were the largest in industries where union wage gains were the highest and

unionization rates were the largest; that is, a 10 percent point increase in an

industry's average unionization rate caused an additional decline in equity value

of 46-87 percent. They also confirmed that labor-intensive firms suffered from

the largest declines in equity value from union organization elections, and the

presence or absence of right-to-work laws had no effect on a firm's expected

losses. Finally, the hypothesis of union spillover effects was supported by their

study.

On the other hand, using a sample of about 1200 large private

manufacturing and nonmanufacturing firms, Becker and Olson (1989) analyzed

the relationship between unionization and firm performance in different aspect

from that of Ruback and Zimmerman (1984) and Bronars and Deere (1990).

Specifically, they examined how union and nonunion firms differed in their

allocation of business risk and financial performance between shareholders and

employees over the period 1970-81. By combining unionization data from the

1977 Annual Return-Report of Employee Benefit Plan (form 5500) with CRSP

(Center for Research on Security Prices) stock price and return data base and

by applying the OLS technique, they estimated the shareholder returns as a

30

function of stockholder systematic risk, unionization and sometimes two-digit

industry dummies. Their results showed that risk-adjusted returns earned by

shareholders in unionized firms were significantly lower than those earned by

shareholders in nonunionized firms.

One of the studies which examined the relationship between unionization

and profitability in a different aspect from the standard approach came from

Abowd (1989). By using a sample of both manufacturing and

nonmanufacturing firms, Abowd Investigated how the unexpected changes in

union labor costs were related to unexpected changes in the market value of

the common stock of the employer firm. More specifically, the purpose of his

study was to find the answer to the following two questions: (1) was there a

negative and a dollar-for-dollar tradeoff between these two variables?, (2) did

collective bargaining maximize the sum of shareholder's wealth (shareholder's

wealth was measured by the value of common stock of the firm) and union

members' wealth (union wealth change was measured using the wage rate

growth method)? In his methodology, first, he converted information on wages

rates, settlement data, unit size, and industry classification from collective

bargaining agreements Into an estimate of the labor cost. Second, he

estimated the present value of the total labor costs that decompose the cost of

realized collective bargaining into expected and unexpected components.

Third, he estimated the unexpected change in shareholders' wealth by using

security price movements around the time of settlement. Finally, by applying

31

the OLS technique, he regressed unexpected changes in union labor cost on

the unexpected changes in common stock value of the firm. His findings

indicated there was a negative and a dollar-for-dollar tradeoff between the value

of common stock and unexpected changes in collective bargaining labor costs.

He also found that the collective bargaining maximize the sum of shareholders'

and union members' wealth.

Unfortunately, there is no study which investigated the impact of

unionization on only regulated manufacturing firms despite the fact that there

has been explosive research on the issue of union-profitability. However, there

are some studies which investigated the unionization-profitability relationship for

regulated trucking and airiine firms. Among these studies, for a small number

of pre-regulated trucking firms. Rose (1985a) partly examined the relationship

between a firm's share price responses to regulated reforms and unionization.

By using 15 firms which were continuously trading general commodity carriers

and 6 Interstate Commerce Commission events which were related in entry and

rate over the 1977-79 period, he estimated the share price responses to

regulatory reforms as a function of unionization (percentage of traffic handled

by union drivers) and other company's operation characteristics. He found that

unions had a negative effect on share price responses to entry and rate events.

His findings supported the hypothesis of union rent sharing, and also confirmed

32

that unions were not expected to reduce their claim proportionally with a loss of

monopoly rents because of the deregulation of regulated trucking firms.^

Similarly, in another study. Rose (1985b) found support for the

conclusion which stated that deregulation caused the reduction of the union's

share of pro-regulation rents by examining the wages of unionized workers In

trucking industries. On the other hand, the study of Hendricks, Feuille and

Szerszen (1980) showed that unionized labor in airline firms captured the part

of regulation rents while deregulation of airiine industry would have little impact

on the power of organized workers. In later study. Rose (1985c) argued that

the previous studies, which examined the relationship between union-nonunion

wage differentials in trucking industries in order to find the share of unions in

regulated industries, might help to quantify the magnitude of union's effect on

wages, but they did not clearly reveal the spline of rents between the union and

trucking companies. He also argued that his previous study (Rose, 1985a)

could provide only a lower bound on rents captured by the union. Therefore,

he tried to complete the shortcomings of his previous study by using a more

appropriate profit measure and equations for the sample of 21 trucking firms.

In addition to Rose's study, some studies, which investigated the relationship between union-nonunion wage differentials, found that some portion of rents which resulted from regulation of some industries were captured by organized labor. For instance, Moore (1978) found that the oven/vhelming portion of the regulation rents in the trucking industry was captured by unions. Another similar example is in the study of Hendricks (1975) which suggested that unions had big power to get part of the rents in regulated electric utilities.

33

In his study, he first used five sample periods: 1973, 1974-75, 1976-77,

1978-79 and 1980-82. Then, he estimated his equation over the regulatory

period, 1973 through 1977 because he expected that the 1978-79 and 1980-82

results would be quite different since in these periods there was deregulation in

the trucking industry. He regressed the ratio of excess market value to sales

on unionization rates, the proportion of total revenues accounted for by motor

carrier revenues, the ratio of predicted loss in firm value to intercity motor

carrier revenues, a dummy variable equal to 1 if the firm is a general freight

carrier and the ratio of firm average number of employees to motor carrier

sales. In general, his estimation results showed that teamsters receive

between 60 and 70 percent of regulated trucking industry rents.

2.3 Summary of Literature

Despite the studies concerning the relationship between unionization and

economic performance that has existed for a long time, the relationship

between profitability and unionization has been examined directly since the last

decade. The studies of Freeman (1983) and Clark (1984) were the first to

investigate the union effect on profitability under industry and line of business

level unionization measures, respectively. The findings of both studies are that

there is a negative and significant union effect on profitability whereas the

magnitude of this effect differs depending on the measures of unionization and

profitability and on empirical methodologies.

34

After these two pioneering studies, explosive research started to extend

the underiying relationship under a different sample of data, different profitability

measures such as accounting profit type and market value type, and different

unionization measures. However, the main findings of Freeman and Clark are

generally supported while the size of the union effect differed in virtually all

studies. Some examples of the firm level analysis include the studies of

Ruback and Zimmerman (1984), Voos and Mishel (1986b), Hirsch and Connolly

(1987), Hirsch (1990, 1991a, 1991b), Bronars and Deere (1990), and Becker

and Olson (1989, 1992).

In general, the existing studies produced satisfactory results for the

negative union effect on profitability. But, the size of the negative effect differed

depending on the sample of data, profit measures, union measures and

empirical methodologies used. However, the findings of virtually all the studies

suffer from the problems caused by the unobserved or omitted determinants of

profitability which are correlated with the union measures because the sample

of data used in these studies was generally not appropriate to allow for the

estimation technique capturing such determinants. In addition, since the

samples of data used significantly differed from study to study, it is quite difficult

to compare the findings of one study with those of another study in terms of the

sensitivity of profit-union relationship to the measures of profitability and

unionization, and to the estimation techniques. Undoubtedly, to overcome this

35

problem, the best strategy is to apply all possible of measures of unionization

and profitability, and the estimation techniques only under a sample of data.

CHAPTER III

METHODOLOGY AND DATA

The purpose of this chapter is to present the empirical procedures and

the analytical tools which will be employed in this study in order to investigate

the relationship between firm profitability and unionization. This chapter

consists of two subsections. In the first subsection, econometric models are

described and discussed to explore the association between firm profitability

and unionization. In the final section, the data sets used and the variables

constructed from them are presented.

3.1 Methodology

This study utilizes the standard profitability equation which is widely used

by Hirsch and Connolly (1987) and by Becker and Olson (1992) among others

in order to examine the relationships among union coverage, firm profitability

and other determinants of firm profitability. To make the findings on the

relationship between union status and profitability more comparable to the

results of previous studies, the specification of the profitability equation used by

previous studies is followed. The standard definition of profit (Karier, 1985) is

presented in the following equation (3.1).

36

37

PR-QP-RK-WL-OE, (3-1)

where 0 is firm output, P is the price of firm output, R is firm depreciation rate,

K is the value of firm capital, W is average wage in firm, L is labor hours in firm

and OE is other expenditures of firm such as material cost. If equation (3.1) is

reordered in terms of profit per sale, the following equation (3.2) results.

PR__QP-RK-WL-OE (3.2) TR TR ^

where total revenue, TR, is equal to PQ.

In this study, three distinct measures are employed for firm profitability

separately; the price-cost margin, the rate of return on invested capital and

excess value.^ As is well known, the first two measures are based on

accounting profits while the last one is based on a combination of accounting

and market-based profits. The rationale for adopting three separate measures

in this study is twofold. The first one is to be able to compare it with the bulk of

prior work. In literature, satisfactory agreement in choosing the appropriate and

unique measure for profitability has not been yet reached by the empirical

In addition to those profit measures, the use of Tobin's q was attempted. However, due to the many missing observations for calculating profit measure it was not used.

38

studies. In practice, the utilized measures have changed from study to study

because of the lack of availability of data on such measures and of the

researcher's preference for the chosen measures. For instance,

accounting-based measures such as price-cost margin and return on

investment have been widely used by Freeman (1983), Clark (1984), Karier

(1985), Voos and Mishel (1986a, 1986b), Hirsch and Connolly (1987), and

Becker and Olson (1992). On the other hand, in their studies Connolly, Hirsch

and Hirschey (1986), and Becker and Olson (1992) have used a hybrid

measure which is based on the combination of accounting-and market-based

profits.

Although several profitability measures have been examined in practice,

none of the measures used in the literature is found to be entirely unambiguous

and satisfactory. The principal advantages and disadvantages of each of the

possible profitability measures have been extensively discussed by the previous

studies. For example, one of the disadvantages of using price-cost margin as

an acceptable profit measure has been pointed out by Weiss (1974). Weiss

argued that the price-cost margin is an acceptable definition or a satisfactory

measure for profitability as long as variations in the value of the capital-sale

ratio are controlled. Similarly, Fisher (1987) argued that even under constant

returns, the profit-sales ratio cannot be equal to the price-cost margin because

of the problems connected with the valuation of capital. Then, he proposed to

use a non4lnear transformation [In(l-profiVsale)] instead of profit-sale ratio as

39

price-cost margin. But, by using Fisher's measure as the price-cost margin,

Machin (1991) found no clear-cut difference between the standard and Fisher's

proposed measures.

On the other hand, one of the principal advantages of using the

price-cost margin measure for profitability is its availability on the firm level. In

order to avoid making a biased choice among the profit measures, all three

measures are used separately.

The firm profitability will be treated as a function of a set of explanatory

variables including firm specific variables, industry specific variables and 16

two-digit industry dummies, which will then be augmented by a union variable

so that the basic estimated equation will be

6 2 1(

/f«i k•^ k-^

M e . iw

VPoyf^Pi/f 'iy^^E P2/*f^E Psy/w / - E P4/w2;/« eyf (3.3)

j=1...,N t=1...,T,

where k and t index the number of periods and the number of variables in the

corresponding groups, respectively, and j indexes the firm. In this study, three

models in terms of firm profit measure, O , are used to estimate the relationship

For discussion on the advantages and disadvantages of price-cost margin measure, see Liebowltz (1982), Collins and Preston (1968), and Cowling and Waterson (1976).

40

between the firm profitability and unionization. The first model employs

the price-cost margin (PCM) as a dependent variable. The second model

utilizes the rate of return on invested capital (ROI) as a measure of profit.

Finally, the third model uses excess value (EV) as a proxy for firm profit.

Under this model formulation, Oj, represents the profitability of firm j in

period t. Poj, Is an intercept. Z j is firm (or industry) union measure such as the

ratio of workers who are covered by union contracts to the total number of

employed workers of the firm (F-UN) (or the proportional of eligible workers who

are covered by union contracts in each firm's principal three-digit industry

(l-UN)) and p j, is its estimated coefficient. Zgjkt includes firm specific variables

such as the natural logarithm of capital-sale ratio (FCAP/S), firm sales growth

(GROWTH), advertising expenditures (ADVES/S), research and development

expenditures (RDES/S), natural logarithm of number of employees (FS) and the

market share of firm (MSF)."* pgjkt 'S the estimated coefficient vector

corresponding to a matrix of firm specific variables. Z3jkt represents industry

specific variables and ^^^ is their estimated coefficient vector. The industry

^As defined in Becker and Olson (1992), excess value measures the difference between the capital market valuation of firm assets reflecting their future profit potential and their cost.

" For the argument that research and development expenditures should be treated as current expenditures or investments in intangible assets, see Mansfield (1968), Bally (1972) and Branch (1974). In addition, see Palda (1964), Peles (1971), Schmalensee (1972), Mann (1974), Brozen (1974) and Comoner and Wilson (1979) for the argument that advertising expenditure should be treated as current expenditures or investments in intangible assets.

41

specific variables included in Z^^^ are the ratio of value of four-digit industry

inventory to value of four-digit industry shipments (IINVA/IS) and annual growth

rate in the four-digit industry sale (IGROWTH). The variables included in Z^-^^^

are the 16 two-digit industry dummies and p>^^ is the corresponding estimated

coefficient vector. Finally, EJ, is an error term with zero mean and constant

variance. Here, all variables except FCAP/S and FS are in linear form.

FCAP/S and FS are in natural logarithm form.^

All three profit models will be run for the same independent variables

except FCAP/S. While the first two models do incorporate FCAP/S ratio as an

explanatory variable. Model III does not include FCAP/S ratio^. So, the simple

specification of each of three models without 16 two-digit industry dummies will

be of the following form:

Model I:

PCM = Bo-B,iUN^^B^i^^)-B,{GROWTH)^B^i^^^yBs{^^)

^Be(FS) •'B.iMSFj -^BeC-^) B^{IGROWTH) +E.

^FCAP/S and FS variables are taken in natural log form since some observations in these variables were unevenly scattered; relative to the mean values of these variables.

^Becker and Olson (1992) found that FCAP/S variable did not have any economic impact on EV. They argued that the economic and statistical significance of the unionization variable did not change when they add FCAP/S variable to their estimation specification.

42

Model II:

ROI = B; B;{UN) ^ B ; ( ^ ) .B;(GRomH) .BK ^^^^^) ^B;{^^^)

If l l j

Model III:

i*v^o^u/T-LJv.T,-/>^0\/E5v ^**,RDES, EV = Bl'^B;\UN)^Bl\GROmH)^B;\^^^^^^^yBl\^^^^^^)^^^^^ s s

IINV, Bi{MSF)^B:r\^yBl\IGROWTH)^E*\

where UN represents firm union coverage (F-UN) or industry union coverage

(l-UN) or dummies which are constructed from firm union coverage and industry

union coverage.

Before presenting the empirical procedures used to estimate the above

models, it is appropriate to review the association between profitability and

explanatory variables. The hypotheses associated with firm profitability and

explanatory variables are as follow;

Unionization measure (UN): the relationship between unit costs (as a

result of increase in union wage) and profit rates depends on the nature of the

bargaining curve settlement (wage-employment pair lies on labor demand or

contract curve), the product demand elasticity and market structure. As

discussed by Clark (1984), under the case that wage-employment outcomes lie

43

on the labor demand curve (in respond to union wage gains, the firm adjusts

upward along its labor demand schedule), the association among all chosen

profitability measures and unionization measures can be negative unless

increase in union-labor productivity is grater than union wage increase. On the

other hand, if unions increase productivity, the relationship between unionization

and profitability can be positive or negative depending on the elasticity of

substitution.

If the wage-employment pair lies on the contract curve and if there is no

offsetting productivity increase, the rate of profit falls regardless of which

profitability measure is used. An efficient bargaining situation on a vertical

contract curve implies that union and firm will maximize the total value of the

enterprise (the sum of firm profits and union rents) and then barging over the

division of the profit. Under this barging regime, the firm makes production

decisions as though it faced the union wage. Thus, level of employment, the

stock of capital, the level of output and price level are unchanged after

unionization. Only the wage varies along the contract curve. Since the level of

employment, output and prices and the stock of capital are not affected by

union, given product demand and the opportunity cost of labor, a union wage

increase, with no offsetting productivity increase, will decrease the profit rate,

whether measured by PCM, ROI and EV (Addison and Hirsch, 1989). The

presence of union productivity effects in the bargaining contest may lead to

ambiguous results. Because the firm makes its decisions as though it faced

44

nonunion wage rates, the union productivity effect has the same impact as

would neutral technological change in the nonunion setting. The firm behaves

as though marginal costs had declined (and output had increased). When the

elasticity of product demand exceeds one, the ratio of capital to labor will be

constant because both inputs increase proportionally. If the productivity effect

is large enough, it may increase total profits sufficiently to leave the firm's PCM

unchanged after division with the union. However, ROI and EV will fall since

the stock of capital will increase.

Measure of capital intensity (FCAP/S): the ratio of firm capital investment

to sales serves as a proxy for the firm capital intensity. There is no certain

hypothesis regarding the coefficient on FCAP/S. The FCAP/S is included in the

first two models (PCM and ROI) to account for differences in firm returns due to

differences in capital intensity. The inclusion of capital intensity measure in EV

Model is more speculative, because capital intensity is measured In the

denominator of EV (Hirsch and Connolly, 1987).

Growth in firm and industry sales (GROWTH and IGROWTH): firm sales

growth and industry sales growth are included in all three models to capture the

changes in firm-level and industry-level demand. If industries and firms

experience large increase in demand, firms can potentially capture short-run

rents until new firms enter the market or existing firms expand their productivity

capacity. High growth rates might also indicate more recent and productivity

capital which may be more profitability than older capital. Under above

45

conditions. It is accepted to have a positive association between all chosen

short-run and long-run profit measures and GROWTH (or IGROWTH).

Advertising expenditures (ADVES/S): advertising expenditures are

defined as the ratio of the stock value of firm's advertising expenditures to firm's

sales. The common argument was that when advertising expenditures create

the product differentiation barriers to entry, there is a positive association

between advertising expenditures and profitability.

On the other hand, the positive correlation between the advertising

expenditures and profitability would be statistically insignificant when the effects

of advertising on profitability are assumed to be very long lived, implying that

when depreciation rate of advertising intensity is very low.

Research and development expenditures (RDES/S): research and

development expenditures are defined as the ratio of the stock value of firm's

research and development expenditure to firm's sales. Economies to scale

barriers have been postulated to arise from research and development

expenditures because investment in it is costly, risky, and subject to economies

of specification in personnel and equipment (Phillips, 1966; Mueller and Tilton,

1969). By taking advantage of high-yield, high risk research and development

opportunity, the firms may increase their profits in two ways. First, profit

relative to firm size may be increased by an above-average return on research

and development projects. Second, research and development created new

products may permit increased growth. Also, to the extent that capital market

46

imperfections exist, the need to undertake large scale research and

development investments can also be a source of absolute cost barriers

(Grabowski and Mueller, 1978).

Firm size (FS): firm size is defined as the number of employees.

Although average firm size may be an important determinant of profit rates, the

direction of the effect is unclear a prior. If we assumed that Baumol's

proposition (1959) which states that large firms have all of the options of small

firms and can invest in lines requiring such scale that small firms are excluded,

is true, the effect of firm size on all three profitability measures should be

positive. On the other hand, if Liebenstein's argument (1966), which states that

X-inefficiency imposed productivity in large firms, is valid, it should be expected

that the association between firm size and profitability measures should be

negative.

Market share of the firm (MSF): market structure in all three models is

characterized by market share of the firm, defined as the firm's sales divided by

four-digit industry sales. If it is assumed that firms with high market share

individually or collectively elevate prices by restricting output, someone could

expect that the association between the profitability and market share is

positive. If the sources of profitability are related more to firm-specific factors

than to the size distribution of competitors, it is possible to expect an

inconclusive association between the market share and the profitability

47

(Hirschey, 1985). Therefore, the expectations on the signs of market share of

the firm for all three models are ambiguous.

Inventory investment (IINV/VIS): inventory investment is defined as the

ratio of the value of industry inventory to the value of industry shipment.

IINV/VIS is used to capture the effect of industry-specific and/or economy-wide

business cycles. It is hypothesized that the relationship between IINV/VIS and

profitability is negative.

In this study, three different estimation techniques are exercised for each

model in order to exploit the relationship between firm unionization and

profitability: ordinary least squares, OLS, technique for cross-section data and

panel data separately, the fixed-effect technique for panel data and the

random-effect technique for panel data.

The first approach is to estimate equation (3.3) using the OLS technique

on cross-section data only for one year. Since the cross-section data of 1988

is the most recent data in our sample, the preference is to regress equation

(3.3) only for this year. Moreover, the main purpose in estimating equation

(3.3) for the cross-section data of 1988 is to compare the results with those of

the previous studies which have used cross-section data. However, because

the number of observations in the cross-sectional sample is changing between

85 and 102, the OLS estimates could suffer from the shortages of degrees of

freedom, implying that the information obtained from this sample is not

48

completely enough to meet the information requirements of the specified

models.^

In practice, the conventional way to avoid the inherent problems

associated with the use of data sets containing a smaller number of

observations, and also to improve the efficiency of the cross-section estimates

as well is generally accomplished by using panel data. Since the sample

consists of both cross-section and time series observations, the second

approach will be to apply the OLS technique to equation (3.3) under the panel

data. However, In addition to the above advantages of using panel data. It

does not necessarily follow that the application of the OLS technique to panel

data can always provide a perfect solution or reduction of all econometric

problems. The key econometric problem would be that the estimates under the

application of the OLS technique to panel data and cross-section data could be

biased when the omitted (or unobserved) determinants of firm profit are

correlated with the included explanatory variables in a profit equation such as

firm unionization. If this is the case, then the biased estimated coefficients

would destroy the conventional b.l.u.e. property of the OLS estimators.® One

way to account for omitted firm specific or time specific effect is the possible

^See Hsiao (1991) for more information on this topic.

®As shown in Johnston (1984, pp. 260-261), the bias in regression coefficients would be dissipated if the covariances between the excluded and included variables are zero, but the estimated disturbance variance would be biased and so that the statistical inferences would still be misleading.

49

inclusion of detailed control variables in all models. When panel data is

available, fixed-effect technique (the observed variables are transformed by

subtracting out the appropriate time-series means or cross-firm means, and

then applying the OLS to transformed data) or least squares dummy variable

technique (dummies are used to capture the time specific effects or firm specific

effects) is another way to eliminate omitted or unobserved firm specific or time

specific effect.® To increase the likelihood of obtaining unbiased estimates,

especially in the fixed-effect technique, the profitability equation is specified

such as that there may be some omitted or unobserved variables whose value

remains constant across individual firms at a given time, but exhibits variation

through time (time effects) or whose value remains constant through time for a

given individual firm but varies across individual firms (individual effects).

Obviously, here the main objective of applying the fixed-effect technique will be

to capture the possible effects of omitted or mismeasured firm specific, industry

specific, or economy-wide variables which are correlated with the firm

®Both approaches in their specifications allow for the intercept term to vary over time and to vary over cross-section units. If the slopes are to vary as well, pooling data would be inappropriate and each separate cross-section regression would involve a distinct model or a single equation model whose all the coefficients are treated as different for different cross-sectional units in different time periods. However, since the number of the parameters which will be estimated will be greater than the number of observations, it is not possible to estimate any meaningful coefficient. But, it is possible to allow for random variation in slope parameters and then to estimate them in an efficient manner by using the random-coefficient model of Swamy (1970). For more details for the random-coefficient model, see Hsiao (1991, pp. 131-153) and Swamy (1970, pp. 311-323).

50

unionization and profitability.^^ Here, the length of the time period (three

years) is not enough to lead the parameters of the model to change

significantly. Therefore, we necessarily assume that the slope coefficients of all

explanatory variables are constant over the period 1986-88. Thus, under this

assumption by employing the OLS technique to panel data, the following

specification is estimated to allow a varying intercept over time or across

individual firms and a common vector of slope coefficients over time and across

individual firms.

e 2

<l>/r-*rBiy,{Zi -Z, Y: B^ Z^ tr' yw) E BsyJ- s/ - /w) H^r^i (3-4) ^-1 k'^

^^he effects of all omitted or unobserved variables are classified by three types of variables. First, the effects of those omitted variables that are specific to individual cross-section units (or firms) but stay constant over time. Examples of such omitted variables are attributes of individual-firm management, ability, gender and socioeconomic background variables. Second, the effects of those omitted variables that are specific to each time period but are the same for all cross-sectional units. Examples of such variables are industry or economy-wide prices, interest rates, and wide-spread optimism or pessimism. Finally, the individual time-varying variables are the variables that vary across cross-sectional units at a given point in time and also exhibit variations through time. Examples for those variables are attributes of firm profits, sales and capital accumulation (Hsiao, 1991).

51

or

6 2

where j=1..,N, t=1986,1987,1988; 0,=(1/N)I%0j„ Op(1/T)Z\^iOj,;

Z,.=(1/N)Z%Z,,, Z,=(1/T)lViZ,,; Z,^=(VN)t'.__,Z^,„ Z^,={m)i:\,Z^^;

Z3^=(i/N)z%Z3j^, z,^=(^^•)t'•^__,z,^^ £ , = ( I / N ) I % £ J , £J=(I/T)ZV,£J,

The difference between two specifications results from the construction

of the means of the variables which represents time-effects or individual-effects.

The computation method of the mean of the variables under both specifications

is performed in the following manner. In the first specification with time-effects,

each variable is averaged across the firms for a given year while in the second

specification with individual-effects, each variable is averaged across three

years for a given firm.

On the other hand, since the fixed-effect technique provides unbiased

and consistent parameter estimates, the central issue associated with pooling is

one of efficiency. So, in the fixed-effect estimation technique, the effects of

omitted firm specific (or time specific) variables are treated as fixed over time.

Therefore, the case may be that the individual-effects or time-effects like ej,

could be random variables. If this is the case, then the fixed-effect estimation

technique may not yield efficient parameter estimates. Therefore, in addition to

the fixed-effect technique, the random-effect technique is applied to the

52

profitability equations." This technique allows for the possibility of comparing

the results of both techniques (fixed-effect and random-effect techniques) to

each other. The estimation of the random-effect technique is a generalization

of the weighted least squares technique since it weights observations in an

inverse relationship to their variances. A two-stage estimation process is used

to obtain the weighing. In the first stage, the OLS will be run on the entire

pooled sample for equation (3.3). Then, the OLS regression residuals are used

to calculate a sample estimation of the variances' components. In the final

stage, the estimated variances obtained from the first step are used to obtain

the generalized least-squares parameter estimations. ^

In addition to the association between firm unionization and profitability,

the source of union gains is an important issue and has been in dispute for a

long time. The previous studies on this topic generally found that negative

union effects were restricted to highly concentrated industries (Freeman, 1983;

Karier, 1985; Salinger, 1984). With subsequent research, the conclusion that

industry concentration provides the major source for union rents appears to be

incorrect and the source of union gains may relate to more firm-specific factors

"Under the random-effect technique, the error term from equation (3.3) is composed of three effects; e,,=|Lij+X,+T|j„ where [i is the individual effect, X is the time effect and TJ is the purely random effect. If there is only individual effect, Ej will be equal to the sum of |Xj and r\.^. On the other hand, if there is only time effect, e, will be equal to the sum of \ and \.

^ More of the details are available in Johnston (1984) and in Rats User's Manual.

53

than to the size distribution of competitors (Connolly, Hirsch and Hirschey,

1986). More recently, Hirsch and Connolly (1987) argued that quasi-rents on

intangible assets are more Important source of union gains than monopoly

profits. This situation could cause unionized firms to have lower research and

development investments than do similar nonunion firms. This argument was

later confirmed by Hirsch (1991b, 1992). Furthermore, while Hirsch and Link

(1987) found product innovation activity to be less important among a sample of

unionized businesses than among similar nonunionized businesses, Acs and

Audretsch (1988) found fewer innovations in highly unionized industries. These

previous findings provide the motivation to investigate whether the research and

development expenditures are still an important source of union rent sharing

behavior. Therefore, this hypothesis will be tested over the period 1986-1988.

However, the validity of this hypothesis is not examined for any individual year

(cross-section) because of many missing observations in each individual year

which may produce less accurate results.

Finally, the possibility of bias arising from the simultaneous determination

of profits and unionization is considered explicitly. The issue of simultaneity

and the concomitant property of simultaneous equations bias in the estimated

unionization-profit relationship are raised by Voos and Mishel (1986a) for

manufacturing industries. Under the ceteris paribus assumption, they argued

that the industries In which unions are likely to organize have higher potential

profits than other industries. Among firm level studies, by employing collective

54

bargaining agreements in the firm's primary three-digit census industry as a

proxy for firm unionization, Hirsch and Connolly (1987) could not reject the null

hypothesis which states that there is no misspecification from using OLS. They

confirmed that two-stage least squares estimates were similar to OLS

estimates. More recently, Hirsch (1991b) found that there was some evidence

for simultaneity between firm union coverage and Tobin's q, but no evidence for

simultaneity between firm union coverage and rate of return on invested capital

by using the firm level unionization measure. Therefore, he suggested that

exogeneity of firm unionization might not be an inappropriate assumption.

Moreover, although in their studies, Clark (1984) and Becker and Olson (1992)

did not test the endogeneity of unionization at the firm level data, they made the

argument that there is no direct simultaneity between the establishment of

bargaining units in the firms which were probably established a long time ago.

They strongly argued that the measure of firm unionization is cleariy

endogenous if firm decisions about union and nonunion operations are a

function of the union effect on profitability if this investment change the relative

share of unionized workers. And they also continued to argue that even though

the endogeneity is assumed to be between the firm profitability and

unionization, it is almost impossible to capture that endogeneity because no

such data is available which captures the dynamics of investment, and makes a

clear distinction between union and nonunion employment in the firm. Besides

the unavailability of such data, the other major difficulty in accounting for union

55

endogeneity is to identify and measure the instruments that influence union

coverage, but not profitability. Even though those problems may exist in the

sample data, we will still test the endogeneity of union with respect to

profitability for all three models by applying the Hausman specification test.

3.2 Data Description

For the period 1986-88, Standard & Poor's Compustat tape. Current

Wage Developments published by Bureau of Labor Statistics, Annual Survey of

Manufactures and Census of Manufactures published by Department of

Commerce were the major sources for the cross-section and time series

database. The sample were necessarily constructed by matching the firms

whose union coverage information exists in Current Wage Development with

those in Compustat data set. Therefore, all firms in the sample are unionized

to some extent. The number of firms obtained by matching the two data sets is

104. However, because of missing values, the sample for regressions that

include particularly advertising, and research and development expenditures are

smaller than the sample for the other regressions. The data related to firm

profitability measures such as PCM, ROI and EV are drawn from the

Compustat tape. The FCAP/S, GROWTH, ADVES/S, RDES/S and FS are also

obtained from the Compustat tape. In order to compute the stock values of

advertising expenditure, and research and development expenditure, each stock

is weighted in an average of past investment outlays, where the weights decline

56

exponentially as one goes backward in time.^^ In computing the stock values

of such variables, a constant depreciation rate which is recommended by

Grabowski and Mueller (1978) is used. In their study, Grabowski and Mueller

(1978) found that the employing of different depreciation rates did not change

the effects of the values of research and development, and advertising

expenditures on profitability significantly.""^

Data used in computing MSF, the ratio of firm sale to the value of

four-digit industry shipment to which the firm belongs, comes from the Annual

Survey of Manufactures and from Standard & Poor's Compustat tape.

In order to get firm level unionization data. Current Wage Development

and the Compustat tape are used. Firm level union coverage data is

constructed in the following manner. First the number of covered workers is

aggregated across the listed contracts of a given firm from Current Wage

Development. Then, the number of those covered workers is divided by the

total number of employed workers in the firm.

^^For the calculation of ADVES and RDES, we used the equations which are used by Grabowski and Mueller (1978). These equations are as follows: ADVES = I~o(1-^R)Vk. RDES=Z~o("l- A)''at.k. K and X^ are depreciation rates for research and development expenditures and advertising expenditures, respectively, r, and a are research and development expenditures and advertising expenditures in period t, respectively.

" See Neriove and Arrow (1962), Mansfield (1968) and Schmalensee (1972) for the argument which was whether or not research and development and advertising capital are depreciated at a constant proportional rate.

57

Industry level data is added to the sample based on a firm's primary SIC

industry code and associated census industry codes. The industry specific

variables, IINV/S and IGROWTH, are drawn from the Annual Survey of

Manufactures. Industry union coverage data at the three-digit level is obtained

from Curme, Hirsch and Macpherson (1990). Curme, Hirsch and Macpherson

estimated the union membership and contract coverage density for industries,

occupations, states, and metropolitan areas."* Although their estimates are

based on calculations from the Current Population Survey tapes over the period

1983-88, annual estimates only for 1986-88 are based on twelve monthly

surveys in each of three years. In their study, they estimated industry union

coverage density, which is the proportion of eligible workers who are covered

by union contracts, in each firm's principal three digit census-coded industry

over the period 1986-88. In this study, industry union coverage density will be

used rather than union membership density in order to make both firm and the

industry union measures more comparable.

Finally, GROWTH and IGROWTH variables in panel data are deflated by

Gross Domestic Product price deflator to obtain real firm and industry growth

^ In order to calculate membership and contract coverage densities, Curme, Hirsch and Macpherson (1990) used the following method which is also used by Freeman and Medoff (1979), and Kokkelenberg and Sockell (1985): U=(Zi8jCOi/IiCOij)*100 where Uj represents the percentage of union members or workers covered by a contract in group j (where i is industry or occupation or state or metropolitan area), Sjj is a binary variable equal to 1 if worker i is a covered (or union) member, cOjj is the sampling weight assigned by the CPS to worker 1.

58

variables. Since all other variables are defined as the ratio to another nominal

variable of the corresponding year, they are implicit in real terms in panel data.

Therefore, other variables are not deflated or inflated again. In addition, to get

real firm and industry growth variables for cross sectional estimates with 1988

data, those variables are not deflated since data for this estimation are

themselves in static form.

The definitions and sources of all variables are presented in Table 3.1

while the descriptive statistics of all variables for 1986, 1987 and 1988 are

reported in Table 3.2.

59

TABLE 3.1 Variable Definitions and Sources

Variable Description

Price-Cost Margin; Gross operating profits before depreciation and taxes PCM divided by firm's sales.

Rate of Return on The ratio of gross operating profits before depreciation Invested Capital; and taxes to total assets of the firm. ROI

Excess Value; EV

Firm Unionization; F-UN

[(Market value of firm's equity+Book value of debt)-Book value of tangible assets]/Firm's sales.

The ratio of workers who are covered by union contracts to the total number of employed workers of firm.

'Capital; FCAP/S The ratio of net value of plant to firm's sales.

Growth in Firm's [(Firm's sale in t-Firm's sale in t-3)/Firm's sale in t-3]. Sales; GROWTH

The Ratio of RDES The stock value of firm's research and development to Firm's Sales; expenditures divided by firm's sales. RDES/S

The Ratio of ADVES The stock value of firm's advertising expenditures to Firm's Sales; divided by firm's sales. ADVES/S

*Firm Size; FS The natural log of number of employees.

The Market Share Firm's sales divided by the value of four-digit industry of the Firm; MSF shipments in which the firms is located.

The Ratio of IINV to VIS;IINVA/IS

The ratio of four-digit industry inventories In which firms are categorized according to the value of four-digit industry shipments in which firm is located.

60

TABLE 3.1 (Continued) Variable Definitions and Sources

Variable Description

Growth in four-digit [(Value of four-digit industry shipmentSj-Value of four-digit Industry Shipments; industry shipments,)/Value of four-digit industry IGROWTH shipments,].

Industry The proportional of eligible workers who are covered by Union Coverage; union contracts in each firm's principal three-digit l-UN census-coded industry.

* Only variables in natural log form.

Sources: PCM and EV: Compustat, Becker and Olson (1992); ROI: Compustat, Brunello (1992); F-UN: Current Wage Developments and Compustat; FCAP/S: Compustat, Brunello (1992); GROWTH: Compustat, Connolly, Hirsch and Hirschey (1986); RDES/S and ADVES/S: Compustat, Salinger (1984); MSF: Compustat and Annual Survey of Manufactures, Hirsch (1991); IINV/VIS: Annual Survey of Manufactures, Census of Manufactures; IGROWTH: Annual Survey of Manufactures, Hirsch (1991); l-UN: Curme, Hirsch and Macpherson (1990).

Note- All variables are calculated over 1986-88. RDES=r,+(1-.10)r,.i+ (1-.10)^2; ADVES=a,+(1-.30)a,.,+(1-.30)%.2; r, and a, are research and development expenditures and advertising expenditures in period t.

TABLE 3.2 Descriptive Statistics of the Variables* (Standard Deviations in Parentheses)

61

Variables

PCM

ROI

EV

F-UN

FCAP/S

GROWTH

RDES/S

ADVES/S

FS

MSF

IINVA/IS

IGROWTH

l-UN

1986 Mean

0.123 (0.058)

0.146 (0.063)

0.191 (0.292)

0.271 (0.257)

-0.503 (0.320)

0.148 (0.341)

0.060 (0.045)

0.026 (0.050)

1.412 (0.509)

0.215 (0.213)

0.158 (0.101)

-0.019 (0.118)

0.434 (0.261)

N

104

104

104

104

104

104

82

93

104

104

104

104

104

1987 Mean

0.135 (0.063)

0.153 (0.058)

0.206 (0.289)

0.230 (0.218)

-0.492 (0.274)

0.243 (0.249)

0.044 (0.045)

0.024 (0.051)

1.423 (0.486)

0.220 (0.219)

0.170 (0.200)

0.102 (0.204)

0.428 (0.245)

N

104

104

95

104

104

104-

104

104

104

104

104

104

104

1988 Mean

0.130 (0.066)

0.144 (0.057)

0.276 (0.358)

0.196 (0.180)

-0.476 (0.273)

0.188 (0.204)

0.051 (0.047)

0.023 (0.050)

1.420 (0.449)

0.216 (0.225)

0.157 (0.112)

0.120 (0.111)

0.414 (0.255)

N

104

104

102

104

104

104

92

104

104

104

104

104

104

62

TABLE 3.2 (Continued) Descriptive Statistics of the Variables* (Standard Deviations in Parentheses)

Note: *N represents the number of observation in the sample. The means of all variables over the period 1986-88 are ; 0.129 for PCM, 0.148 for ROI, 0.224 for EV, 0.232 for F-UN, -0.490 for FCAP/S, 0.193 for GROWTH, 0.052 for RDES/S, 0.024 for ADVES/S, 1.418 for FS, 0.217 for MSF, 0.161 for IINV/VIS, 0.067 for IGROWTH, 0.425 for l-UN.

CHAPTER IV

EMPIRICAL RESULTS AND ANALYSIS

The purpose of this chapter is to present and to examine the empirical

results of the relationship between the firm unionization and profitability, and the

sensitivity of the union-profit relationship to the types of firm profit measures

(PCM, ROI and EV) to the firm unionization measures (F-UN, l-UN, F-DUMMYs

and l-DUMMYs which are constructed from F-UN and l-UN ratios), to the type

of estimation techniques (OLS, fixed-effect and random-effect) and to the

database (cross-section and panel data).

This chapter is divided into two main sections. In the first section, the

OLS results of cross-section data, 1988, for each of three models separately is

presented and analyzed. By applying the OLS technique for 1988, we first

looked at how the firm union coverage ratio, F-UN, and the alternative

measures of firm union coverage ratio, F-DUMMYs, affect PCM, ROI and EV

measures. Then, by using the industry union coverage ratio, l-UN, and

l-DUMMYs, which are constructed from l-UN, as a proxy for the firm union

coverage, the analysis of union-profitability relationship is also performed.

In the second section, we present and investigate three models

throughout the 1986-88 period. This second section is divided into three

subsections. The first subsection starts with the presentation of the OLS results

63

64

for each of three firm unionization measures (F-UN, F-DUMMYs and

l-DUMMYs) and for each of three models separately. Then, the test results for

sources of union gains are presented for each model. This is followed by the

presentation and the evaluation of the results of all three models with firm union

coverage ratio, F-UN, under the fixed-effect technique in the second subsection.

Finally, in the third subsection, the results of all three models with firm union

coverage ratio, F-UN, under the random-effect technique are reported.

4.1 The OLS Estimation Results of the PCM. ROI and EV Models on Cross-Section Data. 1988

Since this study used three distinct firm profitability measures and three

distinct firm unionization measures, a brief outline of the specifications of all

three models is discussed first. In all specifications of each of three models,

the union variable is used as the permanent explanatory variable. Besides this

variable, a number of variables which are important in explaining the firms

profitability are also employed in all three models. In this section, the stock

value of the research and development expenditures, RDES/S, as an

explanatory variable is excluded from the specifications, since many missing

observations in the RDES variable caused the degree of freedom in the sample

to decrease significantly. Moreover, the inclusion of the RDES/S in the

specifications resulted in a statistically insignificant coefficient for this variable in

^While whole sample data covers 102 firm observations, the inclusion of RDES/S on the models caused the sample to be 67.

65

each of three models. Even though the coefficient of RDES/S is found to be

insignificant in the cross-section regressions, the conclusion that the effect of

RDES/S on the firm profitability is insignificant does not necessarily follow.

Because of the data limitations, the regressions could not capture the possible

effect of this variable on firm profit measures. However, it is possible to obtain

a more robust result concerning the effect of this variable on the measure of

firm profitability from the regression equations which will be estimated over the

1986-88 period. In addition, the statistical significance of the unionization

variable is found to be unaffected when the MSF variable is included in all

regressions, implying that the omission of this variable from the regression

specifications has a limited influence on the findings. Therefore, this variable

is excluded from the regression specifications in this section.

In order to observe how unionization and the other explanatory variables

affect PCM, ROI and EV, the following strategy is implemented. In the first

step, each of PCM, ROI and EV models are estimated with each of the union

measures (F-UN, l-UN, F-DUMMYs and l-DUMMYs) separately and with all

other explanatory variables except RDES/S and MSF, for which the results are

shown in the first specification of each table. In the second step, first, the

backward stepwise regression procedure is used in order to search for the best

^However, it is not implied that the market power is irrelevant in explaining firm profitability. This result occurs because the MSF variable is a poor proxy for market power of the firm and the data required to properly calculate an acceptable proxy for market share is inaccessible.

66

subset of explanatory variables to be included in the regressions to obtain

optimal results. In this stage, the backward stepwise regression procedure is

used rather than forward stepwise regression procedure because some

statisticians argue that the backward stepwise search method is preferable to

using the forward stepwise search method for the pool of potential explanatory

variables containing small and moderate numbers of variables.^ In the final

stage of the second step, each model is estimated by including the best subset

of explanatory variables which remains after stepwise procedure into regression

equations, the results of which are reported in the second specification of each

table. In the final step, each model is reestimated by including variables which

are used in the second specification, and the 16 two-digit industry dummies into

regressions, the results of which are found in the third specification of each

table.

To ascertain whether the OLS estimates suffer from heteroscedastic

error terms, two different techniques are applied to all model specifications

separately: Goldfeld-Quandt and White tests.* The calculated F-statistics from

both procedures did not reject the null hypothesis of no heteroscedastic error

terms at the 1 percent level in all specifications of each model. Additionally,

unionization is tested as to whether it is endogenous with respect to each profit

^For a more complete argument about this subject, see Neter, Wasserman and Kutner (1989, p. 458).

"^Goldfeld-Quandt test and White's test are described by Goldfeld, S.M. and Quandt, S.M. (1965) and White, Halbert (1980), respectively.

67

measure. For this purpose, the Hausman specification test procedure is

employed.^ The calculated test statistics for each of three profitability

measures indicate that the null hypothesis which states that unionization is

exogenous with respect to all profit measures is not rejected at any acceptable

level.

The OLS results for PCM, ROI and EV Models with the firm union

coverage variable, F-UN, are reported in Tables 4.1, 4.2 and 4.3, respectively.

In addition to F-UN, each model is reestimated by employing unionization

dummies constructed from F-UN as alternative measure of the firm union

coverage ratio in order to examine the variation in the union effect on firm

profitability over the range of unionization. Therefore, the range of firm

unionization is divided into three categories; F-DUMMY1 for F-UN>0.60,

F-DUMMY2 for 0.60>F-UN>0.30 and F-DUMMY3 for 0.30>F-UN.^ The

regression results using the firm unionization dummies for all three models are

presented in Tables 4.4, 4.5 and 4.6. In Tables 4.7, 4.8 and 4.9, the OLS

estimation results of three models with industry union coverage ratio are

reported. Besides industry union coverage ratio, the same models are also

estimated by employing industry unionization dummies which are constructed

^For the testing procedure, see Hausman (1978) and Rats User's Manual (1992).

®By taking the studies of Hirsch (1991a) and Becker and Olson (1992) as a reference, we divided firm unionization into such ranges.

68

from l-UN. The OLS results with industry union coverage dummies are shown

in Tables 4.10, 4.11 and 4.12.

4.1.1 The OLS Estimation Results of the PCM, ROI and EV Models with Firm Union Coverage Ratio

Table 4.1 shows the OLS results of all specifications for PCM model. As

seen from Table 4.1, union coverage at the firm level on all specifications was

found to be associated with substantially lower short term firm profits, PCM. In

all specifications of Table 4.1, the estimated coefficient of F-UN was found to

be negative and statistically significant at least at the 10 percent level.

Evaluated at the mean, the effect of F-UN on PCM ranged from -6.6 percent to

-10 percent for 100 percent unionized firm.^ The effect of firm unionization on

PCM was found to be not significantly sensitive to the inclusion or exclusion of

the other explanatory variables. Yet, the magnitudes of the estimated

coefficients of F-UN and of the rest of variables became smaller and less

significant when the 16 two-digit industry dummies are included into the

regressions.

^By using the means of variables from Table 3.2, the relationship is evaluated between all profit measures and union variable (and sometimes other explanatory variables) in elasticity form. If both dependent and independent variables (say PCM and F-UN) are in linear form, the average percentage effect of F-UN on PCM is calculated by [Pp.uN(mean of F-UN/mean of PCM)], where PF-UN 'S estimated coefficient of F-UN. On the other hand if dependent variable (say PCM) is in linear form and independent variable (say FCAP/S) is in log form, the average percentage effect of FCAP/S on PCM is calculated by [pFCAP/s(1/mean of PCM)], where PFCAP/S "S estimated coefficient of FCAP/S. For more information on this subject, see Gujarati (1988 pp. 154-155).

TABLE 4.1 OLS Results of PCM Model with Firm

Unionization, 1988 (t-Statistics in Parentheses)

69

Independent Variables

CONSTANT

F-UN

FCAP/S

GROWTH

ADVES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R2

N

Industry Dummies

(1)

0.142* (6.62)

-0.067'' (2.56)

0.049* (4.68)

0.069' (2.43)

0.038 (0.37)

0.034* (2.95)

-0.068 (1.30)

0.078 (1.45)

11.54

0.51

85

no

(2)

0.147* (7.73)

-0.064* (2.86)

0.054* (6.55)

0.076* (2.88)

—

0.030* (3.00)

-0.039 (0.87)

0.082" (1.67)

16.65

0.51

102

no

(3)

0.125* (4.20)

-0.044' (1.75)

0.045* (3.78)

0.054' (1.84)

—

0.030' (2.52)

-0.015 (0.26)

0.068 (0.83)

5.64

0.59

102

yes

* a Significant at the 0.01 level; ''Significant at the 0.05 level; 'Significant at the 0.10 level.

70

From the same table, the control variables, FCAP/S and GROWTH, were

found to have a positive and statistically significant effect on PCM. The

estimated coefficient of FCAP/S in the first specification was found to be 0.049

and implies that a 10 percent increase in the ratio of FCAP/S would cause PCM

to increase by 3.76 percent. Similariy, the estimated coefficient of GROWTH in

the same specification is 0.069 and it is suggested that a 10 percent increase in

GROWTH would cause PCM to increase by 0.99 percent. The findings for

those control variables are consistent with those of most of the previous

studies.

On the other hand, the coefficient estimate of ADVES/S, one measure

of the intangible assets, is found to statistically insignificant in all regression

specifications. In addition to the stock value of advertising, its current

expenditures, and its lag values as a measure of the intangible assets are used

separately. However, the findings did not change significantly, and the

estimated coefficients of advertising became more insignificant. The results of

the effect of ADVES/S on PCM are not consistent with those of Hirsch and

Connolly (1987) and Salinger (1984).

In addition, as pursued in all specifications in Table 4.1, the firm size,

FS, which is measured as the number of employees, is found to have a positive

and statistically significant effect on PCM. From the first specification, the

estimated coefficient of FS is 0.034, implying that PCM is increased by 2.61

percent as a result of a 10 percent increase in FS. The effect of the ratio of the

71

four-digit industry inventory value to the value of industry shipments, IINV/VIS,

which is purposed to capture the effect of the industry or economy wide

business cycles on the short run profit measure, PCM is also examined. It is

expected that the effect of IINV/VIS ratio on PCM is negative. The estimated

coefficient of IINV/VIS ratio is negative as expected, but is not statistically

different from zero at any acceptable level. The coefficient estimate of the

IGROWTH variable is positive and statistically significant at least at the 10

percent level in specification (2), but insignificant in specifications (1) and (3).

When the 16 two-digit industry dummies are included in specification (2), it is

observed that both magnitudes and statistical significance level of the effect of

unionization and the remainder of explanatory variables on PCM became

smaller.

The estimation results of the ROI model which are shown in Table 4.2

reveal that F-UN had a negative and statistically significant impact on ROI. The

coefficient estimate in all three specifications implies that ROI decreases as the

unionization increases, that is, ROIs of the firms that are 100 percent unionized

are changing between -12.3 percent and -13.7 percent.

The estimated coefficient of FCAP/S (=0.021), one of the control

variables, was found to be positive and statistically significant only in

specification (1) at the 10 percent level, implying that there exists a positive

association between FCAP/S and ROI. The coefficient estimate of FCAP/S

from specification (1) demonstrated that a 10 percent increase in this variable

TABLE 4.2 OLS Results of ROI Model with Firm

Unionization, 1988 (t-Statistics in parentheses)

72

Independent Variables

CONSTANT

F-UN

FCAP/S

GROWTH

ADVES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R2

N

Industry Dummies

(1)

0.162* (7.05)

-0.091* (3.25)

0.021' (1.89)

0.021 (0.69)

0.179 (1.64)

0.015 (1.27)

-0.076 (1.36)

0.068 (1.19)

6.03

0.35

85

no

(2)

0.199* (13.3)

-0.101* (4.24)

0.017 (1.56)

—

0.161 (1.46)

-0.089 (1.65)

8.79

0.30

85

no

(3)

0.154* (6.10)

-0.098* (3.42)

0.001 (0.03)

—

0.203° (1.70)

-0.033 (0.56)

3.01

0.46

85

yes

* a Significant at the 0.01 at the 0.10 level;

level; ''Significant at the 0.05 level; 'Significant

73

would cause ROI to increase by 1.45 percent. In contrast to the PCM model,

GROWTH, FS and IGROWTH have insignificant effect on ROI. While both

ADVES/S and IINV/VIS have a nearly significant impact on ROI, the effect was

positive for ADVES/S and negative for IINV/VIS.

Finally, Table 4.3 presents the OLS results for the EV model. In all three

specifications, F-UN was detected to be significantly and negatively related to

EV. The coefficient estimates of F-UN in all specifications suggested that firms

that are 100 percent unionized have a lower EV between 16.7 percent and 26.7

percent as compared to nonunionized firms.

We also found that ADVES/S had a statistically significant and positive

effect on EV in all specifications. The estimated coefficient of ADVES/S was

4.20 in specification (1), 4.14 in specification (2) and 4.01 in specification (3).

Finally, for the rest of explanatory variables (GROWTH, FS, IINV/VIS and

IGROWTH) in specification (1), any significant effect on EV was not detectable.

4.1.2 The OLS Estimation Results of the PCM, ROI and EV Models with Firm Union Coverage Dummies

As seen from the first two specifications of Table 4.4, when all possible

explanatory variables are included into PCM model, the unionization effect on

PCM was negative and statistically significant at the 5 percent level, especially

for the firms whose union coverage ratios are between 0.30 and 0.60. The

estimated coefficient of F-DUMMY2 in specification (1) is -0.035 which indicates

74

TABLE 4.3 OLS Results of EV Model with Firm

Unionization, 1988 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

F-UN

GROWTH

ADVES/S

FS

IINV/VIS

IGROWTH

F-Statistics

R2

N

Industry Dummies

(1)

0.099 (0.82)

-0.285' (1.91)

0.138 (0.85)

4.206* (7.25)

0.064 (0.97)

0.120 (0.43)

-0.058 (0.19)

13.37

0.50

85

no

(2)

0.256* (5.36)

-0.377* (3.01)

—

4.149' (7.40)

39.57

0.49

85

no

(3)

0.133" (2.04)

-0.236* (1.74)

—

4.013* (6.95)

—

—

—

7.46

0.65

85

yes

* ai Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

75

TABLE 4.4 OLS Results of PCM Model with Firm

Unionization Dummies, 1988 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

F-DUMMY1

F-DUMMY2

FCAP/S

GROWTH

ADVES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R2

N

Industry Dummies

(1)

0.134* (6.47)

-0.032 (1.50)

-0.035" (2.47)

0.048* (4.47)

0.080* (2.85)

0.023 (0.22)

0.035* (2.97)

-0.072 (1.37)

0.073 (1.37)

9.98

0.51

85

no

(2)

0.141* (7.57)

-0.036" (1.99)

-0.029" (2.25)

0.053* (6.32)

0.088* (3.37)

—

0.029* (2.75)

-0.041 (0.90)

0.074 (1.51)

13.79

0.50

102

no

(3)

0.116* (4.10)

-0.024 (1.26)

-0.018 (1.26)

0.043* (3.63)

0.065" (2.18)

—

0.029" (2.34)

-0.014 (0.24)

0.070 (0.84)

5.24

0.59

102

yes

* *Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* F-DUMMY1 is F-UN>0.60; F-DUMMY2 is 0.60>F-UN>0.30; F-DUMMY3 is 0.30>F-UN.

76

that the overwhelming portion of the union gain occurs between 30 and 60

percent of unionization.

From all specifications of Table 4.5, the estimated coefficients of the

union coverage dummies also suggest that the effect of union on ROI varies

with the extent of firm union coverage. The coefficients of both F-DUMMY1

and F-DUMMY2 in all specifications are statistically significant at least at the 5

percent level. The estimated coefficients in the first specification are found to

be -0.049 for F-DUMMY1 and -0.042 for F-DUMMY2.

In EV model as in PCM and ROI, the estimated coefficient of

F-DUMMY2 in all specifications is negative and statistically significant, and is

between -0.172 and -0.207. These estimates indicate that union effect on

profitability for the firms that are moderately unionized is greater than that for

the firms that are highly and less unionized.

As seen from Tables 4.4-4.6, besides union coverage dummies, the

estimated coefficients of all other explanatory variables followed the same

pattern as they did in Tables 4.1-4.3. So, moderate unionization which is

represented by F-DUMMY2 (0.30<F-UN<0.60) yields more meaningful result

than high unionization, F-DUMMY1 (0.60<F-UN) and low unionization,

F-DUMMY3 (F-UN<0.30).

11

TABLE 4.5 OLS Results of ROI Model with Firm

Unionization Dummies, 1988 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

F-DUMMY1

F-DUMMY2

FCAP/S

GROWTH

ADVES/S

FS

IINV/VIS

IGROWTH

F-Statistics

R 2

N

Industry Dummies

(1)

0.151* (6.77)

-0.049" (2.13)

-0.042* (2.75)

0.019' (1.68)

0.034 (1.14)..

0.162 (1.45)

0.016 (1.31)

-0.083 (1.47)

0.059 (1.03)

4.99

0.34

85

no

(2)

0.159* (7.31)

-0.048" (2.14)

-0.036" (2.45)

0.017 (1.51)

—

0.140 (1.24)

0.022' (1.74)

-0.110" (1.98)

—

5.91

0.31

85

no

(3)

0.096* (2.99)

-0.045' (1.90)

-0.027' (1.62)

0.007 (0.45)

—

0.178 (1.46)

0.027' (1.97)

-0.052 (0.85)

2.77

0.48

85

yes

* *Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* F-DUMMY1 is F-UN>0.60; F-DUMMY2 is 0.60>F-UN>0.30; F-DUMMY3 is 0.30>F-UN.

78

TABLE 4.6 OLS Results of EV Model with Firm

Unionization Dummies, 1988 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

F-DUMMY1

F-DUMMY2

GROWTH

ADVES/S

FS

IINV/VIS

IGROWTH

F-Statistics

R

N

Industry Dummies

(1)

0.085 (0.74)

-0.148 (1.25)

-0.207" (2.59)

0.189 (1.20)

4.081* (7.07)

0.059 (0.89)

0.125 (0.45)

-0.024 (0.08)

12.22

0.52

85

no

(2)

0.240* (5.74)

-0.223" (2.14)

-0.224* (3.04)

—

4.016' (7.14)

27.61

0.50

85

no

(3)

0.130" (2.21)

-0.167 (1.60)

-0.172" (2.25)

—

3.895' (6.77)

7.38

0.66

85

yes

* a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level. F-DUMMY1 is F-UN>0.60; F-DUMMY2 is 0.60>F-UN>0.30; F-DUMMY3 is 0.30>F-UN.

79

4.1.3 The OLS Estimation Results of the PCM, ROI and EV Models with Industry Union Coverage Ratio and Dummies

While Tables 4.7-4.9 present the OLS results of PCM, ROI and EV

models with industry union coverage ratio, respectively. Tables 4.10-4.12 report

the OLS results of the same models with industry union coverage dummies.

From Tables 4.7-4.9, the coefficient estimates of industry union coverage

ratio are found to be statistically insignificant in all firm profit measures. The

comparison of the estimated coefficients of l-UN in Tables 4.7-4.9 with the

estimated coefficients of F-UN in Tables 4.1-4.3 provides clear evidence that

firm union coverage has strong advantage over the industry union coverage in

terms of supporting the hypothesis of a negative union-profit association.

Obviously, these findings confirm the results of Hirsch (1991a) and Becker and

Olson (1992). By using different time periods, different profit measures and

different estimation methodologies from each other, both studies concluded that

firm union coverage measure had a negative and statistically significant impact

on firm profitability. On the other hand, when industry union coverage ratio is

used as a proxy for firm unionization, they found that unionization had a weak

and statistically insignificant impact on firm profitability.

In all regression specifications of Tables 4.7-4.9, all explanatory variables

except FS are found to be in the same pattern in terms of the magnitudes and

statistical significance levels of the coefficients as those in the previous

regressions with F-UN. Therefore, those variables are not focused upon here.

In comparing the results of the PCM, ROI and EV models in Tables 4.1-4.3 with

TABLE 4.7 OLS Results of PCM Model with Industry

Unionization, 1988 (t-Statistics in Parentheses)

80

Independent Variables

CONSTANT

l-UN

FCAP/S

GROWTH

ADVES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R

N

Industry Dummies

(1)

0.103* (4.52)

0.026 (1.15)

0.052* (4.80)

0.087* (3.06)

0.090 (0.87)

0.046* (4.20)

-0.066 (1.21)

0.020 (0.39)

10.11

0.47

85

no

(2)

0.110* (5.66)

0.025 (1.30)

0.057* (7.07)

0.102* (4.22)

—

0.040' (4.13)

21.50

0.47

102

no

(3)

0.100* (4.16)

-0.001 (0.45)

0.042* (3.76)

0.075* (2.69)

—

0.037' (3.45)

5.91

0.57

102

yes

* a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

TABLE 4.8 OLS Results of ROI Model with Industry

Unionization, 1988 (t-Statistics in Parentheses)

81

Independent Variables

CONSTANT

l-UN

FCAP/S

GROWTH

ADVES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R

N

Industry Dummies

(1)

0.120* (4.91)

0.006 (0.25)

0.027" (2.31)

0.046 (1.49)

0.246" (2.15)

0.031* (2.61)

-0.079 (1.33)

-0.001 (0.01)

3.99

0.26

85

no

(2)

0.124* (5.13)

0.006 (0.26)

0.027" (2.32)

0.046 (1.59)

0.245" (2.19)

0.031* (2.63)

-0.079 (1.37)

—

4.72

0.26

85

no

(3)

0.075" (2.47)

-0.038 (1.28)

-0.010 (0.63)

0.027 (0.83)

0.245" (2.03)

0.036* (2.68)

-0.017 (0.28)

—

2.60

0.46

85

yes

* a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

TABLE 4.9 OLS Results of EV Model with Industry

Unionization, 1988 (t-Statistics in Parentheses)

82

Independent Variables

CONSTANT

l-UN

GROWTH

ADVES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R 2

N

Industry Dummies

(1)

-0.083 (0.69)

0.130 (1.03)

0.208 (1.31)

4.417* (7.58)

0.115' (1.84)

0.112 (0.39)

-0.307 (1.03)

12.52

0.49

85

no

(2)

-0.091 (0.87)

0.095 (0.78)

0.146 (0.98)

4.430* (7.89)

0.125" (2.06)

—

—

18.53

0.48

85

no

(3)

-0.162 (1.41)

0.173 (1.19)

0.002 (0.01)

4.042* (7.07)

0.122' (1.91)

—

—

6.83

0.66

85

yes

* a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

83

those in Tables 4.7-4.9, It is observed that the estimated coefficient of FS is

positive and statistically significant at the 1 percent level only for the PCM

model with F-UN, while it is positive and statistically significant at the 10 percent

level for all three models with l-UN.

Tables 4.10-4.12 explore the impact of unionization on profitability,

utilizing three industry union coverage dummies such as I-DUMMY1,

I-DUMMY2 and I-DUMMY3 instead of industry union coverage ratio. Among

dummies which are constructed from industry union coverage ratio, particularly

I-DUMMY1 and I-DUMMY2 (I-DUMMY3 in intercept) are employed as is

employed for F-DUMMYs. When different combinations of industry dummies

are included in the specifications, the results yield negative and statistically

significant coefficient estimates for I-DUMMY2 and, but not for I-DUMMY1.

For both PCM and EV, from the first and second specifications of Tables 4.10

and 4.12, the estimated coefficients of I-DUMMY2 are found to be negative and

statistically significant at the 10 percent level. So, the industry unionization

spline for all three models except ROI reflected the similar pattern to the results

of firm unionization spline. Here, the significant portion of union gains again

occurs between 30 and 60 percent of unionization.

4.1.4 Summary Assessment for All Cross-Section Estimation Results, 1988

In general, the cross-section results for 1988 provide significant support

for the argument that the level of union coverage at the firm level has a

84

TABLE 4.10 OLS Results of PCM Model with Industry

Unionization Dummies, 1988 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

I-DUMMY1

I-DUMMY2

FCAP/S

GROWTH

ADVES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R2

N

Industry Dummies

(1)

0.121* (5.98)

0.041* (1.03)

-0.015' (1.77)

0.053* (5.07)

0.086* (3.14)

0.074 (0.73)

0.042* (3.97)

-0.059 (1.13)

-0.055 (1.07)

10.27

0.51

85

no

(2)

0.131* (7.30)

0.033* (1.49)

-0.012' (1.89)

0.059* (7.50)

0.106* (4.50)

—

0.037* (3.93)

—

—

19.19

0.49

102

no

(3)

0.116* (4.49)

0.007 (0.34)

-0.017 (1.14)

0.046* (4.01)

0.076* (2.77)

—

0.036* (3.32)

—

—

5.79

0.58

102

yes

* a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* I-DUMMY1 is l-UN>0.60; I-DUMMY2 is 0.60>I-UN>0.30; I-DUMMY3 is 0.30>I-UN.

TABLE 4.11 OLS Results of ROI Model with Industry

Unionization Dummies, 1988 (t-Statistics in Parentheses)

85

Independent Variables

CONSTANT

I-DUMMY1

I-DUMMY2

FCAP/S

GROWTH

ADVES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R

N

Industry Dummies

(1)

0.137* (6.01)

0.007 (0.30)

-0.020 (1.54)

0.029" (2.48)

0.048 (1.55)

0.243" (2.15)

0.029" (2.46)

0.073 (1.24)

0.026 (0.46)

3.96

0.29

85

no

(2)

0.136* (6.29)

0.004 (0.37)

-0.018 (1.44)

0.035* (3.26)

0.058" (2.01)

0.271" (2.50)

0.026" (2.22)

—

—

4.91

0.27

85

no

(3)

0.101* (3.30)

-0.037 (1.41)

-0.049' (1.98)

-0.001 (0.90)

0.030 (0.97)

0.325* (2.74)

0.033* (2.64)

—

—

3.10

0.50

85

yes

* *Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* I-DUMMY1 is l-UN>0.60; I-DUMMY2 is 0.60>I-UN>0.30; I-DUMMY3 is 0.30>I-UN.

86

TABLE 4.12 OLS Results of EV Model with Industry

Unionization Dummies, 1988 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

I-DUMMY1

I-DUMMY2

GROWTH

ADVES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R

N

Industry Dummies

(1)

0.019 (0.18)

0.241* (1.44)

-0.113' (1.77)

0.204 (1.35)

4.319* (7.79)

0.092 (1.54)

0.015 (0.56)

-0.087 (0.30)

13.15

0.54

85

no

(2)

0.030 (0.32)

0.232 (1.48)

-0.120' (1.95)

0.181 (1.26)

4.270* (8.02)

0.101' (1.75)

—

—

18.66

0.54

85

no

(3)

0.014 (0.12)

0.169 (1.33)

-0.168' (1.97)

-0.002 (0.01)

4.260* (7.70)

0.108' (1.77)

—

—

7.70

0.70

85

yes

* *Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* I-DUMMY1 is l-UN>0.60; I-DUMMY2 is 0.60>I-UN>0.30; I-DUMMY3 is 0.30>I-UN.

87

negative impact on profitability regardless of different measures of firm

profitability and firm unionization. The findings, which are based on the most

recent sample data on firm level, are generally consistent with most of the

previous studies. Upon examination of the first specification of all three models

with F-UN in which all explanatory variables are included, it is noted that PCM

is reduced by 10 percent, ROI by 12.3 percent and EV by 20.2 percent for 100

percent unionized firms. These results are consistent with the findings of

Hirsch and Connolly (1987). By constructing the sample from the 1977

Compustat tape, Hirsch and Connolly (1987) found that unionism reduced

Tobin's q by 13 percent-20 percent, rate of return on sale by 11 percent-17

percent.

Furthermore, estimation of all three models suggests that as the

profitability measure changes, the effect of unionization on the corresponding

profitability measure varies to some extent. Obviously, the size of the effect is

found to be larger on EV than on both PCM and ROI. It is also found that it is

less on PCM than on ROI.

Now, question is why the sizes of the effect on profit vary with profit

measures. Actually, if the magnitude of the unionization effect with its

corresponding profitability measure is examined, it can be argued that this

result is related to the time characteristics of chosen profitability measures. As

previously discussed, the first two measures are accounting type profitability

measures, while last one is combination of both market and account type.

88

Since the last profitability measure consists of the components related to past,

present and future, it is generally long-run type profit measure. On the other

hand, the first two are short run type measures because they consist of the

components related to past and present. Therefore, it is possible and natural to

find a higher unionization effect on long-run type of profit measures than that on

short run type of measures.

This study employed four different firm unionization measures, that is,

firm and industry union coverage ratios (F-UN and l-UN) and dummies for F-UN

and l-UN, it is appropriate to compare the results on unionization effects on

profitability with each other. First of all, while F-UN ratio suggests a negative

union effect on profitability, l-UN ratio does not.

When F-UN enters into all regression equations, the estimated

coefficients are detected to be negative and statistically significant. In general,

the estimated coefficients of F-DUMMYs in all regression specifications suggest

that union has significantly negative effect on profitability if the firm union

coverage ratio is especially between 30 and 60 percent (F-DUMMY2).

Yet, when l-UN enters into all regression equations instead of F-UN, the

estimated coefficients of l-UNs for all three models are generally discovered to

be statistically insignificant. On the contrary, a different conclusion results when

l-DUMMYs constructed from l-UN are used as a measure of the firm

unionization. Only the estimated coefficient of I-DUMMY2 is negative and

89

statistically significant at the 10 percent level in PCM and EV models but

Insignificant in ROI.

Consequently, the overall results of this section indicate that the union

effects on profitability differ depending on both measure of profitability and

unionization. When the firm union coverage ratio, F-UN, is used in all

specifications, negative and significant union effects on profitability are found

regardless of the profitability measures, which supports the hypothesis of a

negative association existing between unionization and profitability. On the

other hand, industry union coverage ratio was proxied to firm unionization in all

three models and the findings do not support the hypothesis that there is a

negative relationship between unionization and profitability. So, if the results

from both ratios, F-UN and l-UN, are compared, it can be concluded that F-UN

seems to be a more reliable measure than l-UN in terms of supporting the

hypothesis of negative union-profitability association. On the other hand, a

comparison of the results with both firm and industry union coverage dummies

reveals that both measures provide similar information on the relationship of

profitability-unionization.

4.2 The Estimation Results of the PCM. ROI and EV Models over the 1986-1988 Period

To overcome the idea that cross-section estimates of the union-profit

relationship are likely to be biased, three models are reestimated over the

1986-88 period. As we previously noted, the nature of the panel data allows for

90

control of unobservable individual firm and industry effects in the empirical

analysis. Therefore, it is expected that the results, especially on the association

between unionization and profitability under panel data are more robust than

the previous results. Once more, when including market share of firm (MSF) as

an explanatory variable into the regression specifications, negative but

statistically insignificant coefficient estimates are most often found. This finding

again implies that the market share measure is a poor proxy for the market

structure. Therefore, this measure is excluded from the rest of the empirical

estimations. In addition, in this section the estimation results of the regression

specifications with industry union coverage ratio, l-UN, are unreported because

there were not any statistically significant coefficient estimates of this variable

for all three models in the prior section or in this section. On the other hand, in

the previous section a very significant effect of union on the profitability is found

when l-DUMMYs are entered into the regression specifications as a proxy of

the firm unionization. As a result, the regression equations with l-DUMMYs are

used for all three models in this section as well.

4.2.1 The OLS Results of the PCM, ROI and EV Models over the 1986-88 Period

In this first subsection, applying the OLS technique to panel data, all

specifications of three models described in the first part of this chapter are

estimated. Here, the potential problem is that the disturbance variances of the

regressions may not be constant across firms. Using the Goldfeld-Quandt test

91

procedure, all specifications of three models are tested to confirm whether the

OLS regressions suffer from heteroscedasticity. Not surprisingly, the results

confirmed that all specifications of the three models suffered from this

problem.® Therefore, the White correction technique to the standard errors is

applied for all reported specifications of three models.®

The OLS estimations of all three models with F-UN, F-DUMMYs and

l-DUMMYs are displayed in Tables 4.13-4.21. The first specification of each

table presents the estimations of models with all explanatory variables except

16 two-digit industry dummies. Then, the explanatory variables that are

insignificant at the 10 percent level were eliminated by a stepwise procedure.

The second specification in each table is, therefore, designed to provide the

OLS results of models with some explanatory variables which remain after the

stepwise procedure. Finally, the last specification provides the OLS results of

models with explanatory variables which are used in the second specification

and with the 16 two-digit industry dummies. In addition. Table 4.22 presents

the test results of all three models for the source of union gains.

®ln spite of the fact that the choice of how many central observations (firms) to drop is largely subjective. Following the evidence suggested by Harvey and Phillips (1974), only 1/3 of the observations (firms) are not used.

^White's method is to obtain unbiased point estimates of estimated coefficients for all variables using OLS and then to estimate omega, Q., matrix as a diagonal matrix with the jth squared OLS residuals as the (j,j) th element in omega matrix. If the estimated equation is 0=zp+e in matrix notation. So the formula Var(PoLs)=(Z'Z)-"'z'aZ(Z'Z)"'' is used as a consistent estimator of the variance-covariance matrix of the OLS estimator-regardless of the precise form of the heteroscedasticity (White, 1980).

92

4.2.1.1 The OLS Estimation Results of the PCM. ROI and EV Models with Firm Union Coveraoe Ratio

Tables 4.13-4.15 summarize the results of least-square estimations of all

three models with F-UN variable after correcting the variance-covariance matrix

of the OLS estimators for heteroscedasticity.

From Table 4.13, the effect of F-UN on firm profitability was found to be

negative and statistically significant which is consistent with the results of our

cross-section study. The coefficient estimate of F-UN reported in the first

specification implies that an increase of F-UN from 0 to 100 percent is

associated with a decrease of 7.1 percent in PCM. As seen from the first two

specifications, FCAP/S, GROWTH, RDES/S, FS and IGROWTH have positive

and statistically significant effects on PCM, while ADVES/S and IINV/VIS have

small and statistically insignificant effects on PCM. The coefficient in

specification (1) is estimated to be 0.060 for FCAP/S, 0.062 for GROWTH,

0.020 for FS and 0.080 for IGROWTH. In contrast to the results of the

cross-section estimation, RDES/S is an important determinant of PCM. The

estimated coefficient of RDES/S is positive and statistically significant at least

at the 5 percent level. The coefficient estimates of RDES/S were 0.139 and

0.153 in the first two specifications, respectively. Finally, the inclusion of the 16

two-digit industry dummies causes the magnitude and significance level of the

estimated coefficients of most explanatory variables to be reduced significantly.

For example, the addition of these dummies to the second specification reduces

TABLE 4.13 OLS Results of PCM Model with Firm

Unionization, 1986-88 (t-Statistics in Parentheses)

93

Independent Variables

CONSTANT

F-UN

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINVA/IS

IGROWTH

R

N

Industry Dummies

(1)

0.161* (10.4)

-0.040* (3.46)

0.060* (10.9)

0.062* (5.62)

0.032 (0.80)

0.139" (1.89)

0.020" (2.52)

-0.019 (0.79)

0.080* (3.57)

0.49

204

no

(2)

0.164* (11.9)

-0.042* (4.01)

0.063* (11.7)

0.068* (6.25)

—

0.153" (2.28)

0.020* (2.81)

—

0.070' (1.96)

0.46

243

no

(3)

0.126* (6.64)

-0.024' (1.76)

0.047* (6.35)

0.050* (5.16)

—

0.104 (1.25)

0.029* (3.56)

—

0.058 (1.42)

0.53

243

yes

* a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

94

the absolute value of F-UN coefficient from -0.042 to -0.024 and the

significance level of this coefficient from 1 percent level to 10 percent level.

Table 4.14 reports the OLS results of ROI model with F-UN. The

coefficient estimate of F-UN in the first specification indicates that a fully

unionized firm had a 7.2 percent lower ROI than a nonunionized firm. As found

in cross-section estimations, FCAP/S has a positive and significant impact on

ROI and its estimated coefficient in the first specification is 0.034. Both

GROWTH and IGROWTH which are designed to capture the effects of changes

in the firm and industry level demand have positive and statistically

significant effects on ROI. From the first specification, the statistically

significant coefficients of GROWTH and IGROWTH are 0.061 and 0.077,

respectively. In addition to F-UN, FCAP/S, GROWTH and IGROWTH, the

effect of ADVES/S on ROI is detected to be statistically significant and positive

in all specifications except the last specification. The estimated coefficients of

ADVES/S reported in specifications (1) and (2) are 0.186 and 0.209,

respectively. On the other hand, in all specifications of ROI model, RDES/S,

FS and IINV/VIS appear to have an insignificant impact on ROI. The results

obtained by including industry dummies to the last specification are found to be

similar to previous estimation results.

Table 4.15 reports OLS estimates of EV model with F-UN variable. The

coefficient estimates of F-UN in all specifications are negative and statistically

significant at the 1 percent level. An increase In the firm unionization rate from

TABLE 4.14 OLS Results of ROI Model with Firm

Unionization, 1986-88 (t-Statistics in Parentheses)

95

Independent Variables

CONSTANT

F-UN

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINV/VIS

IGROWTH

R2

N

Industry Dummies

(1)

0.160* (9.72)

-0.046* (3.09)

0.032* (4.99)

0.052* (3.17)

0.186* (3.83)

0.087 (0.88)

0.011 (1.29)

-0.027 (1.06)

0.071* (2.48)

0.29

204

no

(2)

0.170* (18.0)

-0.069* (4.32)

0.021* (3.52)

0.081* (4.77)

0.209* (4.40)

—

—

—

0.010* (5.86)

0.27

258

no

(3)

0.111* (9.44)

-0.052* (2.63)

0.019 (1.33)

0.070* (4.25)

-0.046 (0.70)

—

—

—

0.003' (1.92)

0.46

258

yes

* a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

96

TABLE 4.15 OLS Results of EV Model with Firm

Unionization, 1986-88 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

F-UN

GROWTH

ADVES/S

RDES/S

FS

IINVA/IS

IGROWTH

R

N

Industry Dummies

(1)

0.139" (1.95)

-0.243* (4.14)

0.094 (1.64)

3.030* (9.99)

0.410 (0.59)

0.041 (0.83)

-0.007 (0.08)

-0.064 (0.52)

0.44

173

no

(2)

0.236* (9.90)

-0.338* (6.15)

—

3.634' (9.61)

0.47

228

no

(3)

0.118* (3.16)

-0.222* (3.51)

—

3.173* (5.32)

—

—

—

—

0.58

228

yes

* *Signlfleant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

97

0 percent to 100 percent implies 25.1 percent decline in EV in specification (1),

34.9 percent decline in EV in specification (2) and 22.9 percent decline in EV in

specification (3). In addition to the F-UN variable, among all other explanatory

variables, only ADVES/S, one measures of intangible assets, is found to have

a positive and statistically significant effect on EV. Even though the magnitude

of estimated coefficients of ADVES/S in this subsection are smaller than those

of ADVES/S in our previous cross-section results, these coefficients are still

large in terms of the magnitude. The estimated coefficients of ADVES/S in

three specifications are 3.030, 3.634, and 3.173, respectively.

4.2.1.2 The OLS Estimation Results of the PCM. ROI and EV Models with Firm Union Coverage Dummies

Tables 4.16-4.18 demonstrate the OLS estimates of all three models with

F-DUMMYs, the alternative measures of F-UN. The OLS results of the PCM

model are reported in Table 4.16. From the specifications (1) and (2), the

estimated coefficients of F-DUMMY2 are negative and statistically significant at

the 1 percent level. The estimated coefficient of F-DUMMY2 is -0.024 in

specification (1). Again, it is detected that significant union gains arise

especially over the range of between 30 and 60 percent of unionization.

Besides F-DUMMYs, in both of the first two specifications all other explanatory

variables except ADVES/S and IINV/VIS have positive and statistically

significant impacts on PCM. The coefficients of these explanatory variables

98

TABLE 4.16 OLS Results of PCM Model with Firm

Unionization Dummies, 1986-88 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

F-DUMMY1

F-DUMMY2

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINVA/IS

IGROWTH

R

N

Industry Dummies

(1)

0.158* (10.7)

-0.017 (1.58)

-0.024* (2.87)

0.062* (11.0)

0.068* (5.92)

0.024 (0.60)

0.174" (2.35)

0.019" (2.42)

-0.019 (0.77)

0.085* (3.82)

0.49

204

no

(2)

0.161* (12.0)

-0.019" (2.23)

-0.022* (2.72)

0.063* (11.6)

0.074* (6.38)

—

0.182* (2.66)

0.018* (2.70)

—

0.002 (1.25)

0.46

243

no

(3)

0.121* (6.66)

-0.011 (1.08)

-0.010 (1.12)

0.047* (6.29)

0.065* (5.27)

—

0.119 (1.27)

0.028* (3.54)

—

0.001 (1.16)

0.53

243

yes

* *Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* F-DUMMY1 is F-UN>0.60; F-DUMMY2 is 0.60>F-UN>0.30; F-DUMMY3 is 0.30>F-UN.

99

which are reported are found to be similar in terms of size and sign to those In

Table 4.13.

The results of ROI and EV models with firm union coverage dummies

are summarized in Tables 4.17 and 4.18, respectively. Once more, as seen

from all of the specifications of both tables, the estimated coefficients of

F-DUMMY2 are negative and statistically significant at least at the 1 percent

level. AFrom specification (1) of each table, it was seen that the estimated

coefficient of medium union coverage, F-DUMMY2, was -0.033 for ROI and

-0.168 for EV model. As shown in Table 4.17, in addition to union coverage

dummies, all other independent variables except RDES/S, FS and IINV/VIS

have positive and statistically significant impacts on ROI. Unfortunately, from

Table 4.18, only ADVES/S among all other explanatory variables in the EV

model has a positive and statistically significant coefficient at least at the 10

percent level. Once more, the magnitudes and signs of the estimated

coefficients of statistically significant explanatory variables which are presented

in Tables 4.17 and 4.18 are similar to those in Tables 4.14 and 4.15.

4.2.1.3 The OLS Estimation Results of the PCM. ROI and EV Models with Industry Union Coverage Dummies

The OLS results of PCM, ROI and EV models with industry union

coverage dummies are presented in Tables 4.19, 4.20 and 4.21, respectively.

For the PCM model, the estimated coefficients of medium union coverage,

I-DUMMY2 is negative and statistically significant. While the spline function

TABLE 4.17 OLS Results of ROI Model with Firm

Unionization Dummies, 1986-88 (t-Statistics in Parentheses)

100

Independent Variables

CONSTANT

F-DUMMY1

F-DUMMY2

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINVA/IS

IGROWTH

R2

N

Industry Dummies

(1)

0.157* (9.63)

-0.010 (0.81)

-0.033* (3.36)

0.034* (5.41)

0.061* (3.58)

0.175* (3.52)

0.137 (1.38)

0.010 (1.16)

-0.026 (1.00)

0.077* (2.83)

0.31

204

no

(2)

0.164* (18.4)

-0.036* (2.69)

-0.040* (4.22)

0.020* (3.29)

0.084* (4.71)

0.189* (3.92)

—

—

—

0.010* (5.39)

0.28

258

no

(3)

0.105* (9.63)

-0.035" (2.39)

-0.026* (2.78)

0.020 (1.33)

0.073* (4.37)

0.057 (0.86)

—

—

—

0.004" (1.99)

0.46

258

yes

* *Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* F-DUMMY1 is F-UN>0.60; F-DUMMY2 is 0.60>F-UN>0.30; F-DUMMY3 is 0.30>F-UN.

TABLE 4.18 OLS Results of EV Model with Firm

Unionization Dummies, 1986-88 (t-Statistics in Parentheses)

101

Independent Variables

CONSTANT

F-DUMMY1

F-DUMMY2

GROWTH

ADVES/S

RDES/S

FS

IINVA/IS

IGROWTH

R

N

Industry Dummies

(1)

0.143* (2.17)

-0.150* (3.49)

-0.168* (4.59)

0.126* (2.22)

2.896* (9.57)

0.574 (0.82)

0.026 (0.54)

-0.018 (0.19)

-0.013 (0.11)

0.46

176

no

(2)

0.220* (10.0)

-0.218* (5.80)

-0.196* (6.13)

—

3.499* (9.06)

—

—

—

—

0.43

228

no

(3)

0.089" (2.49)

-0.148* (2.83)

-0.135* (3.34)

0.113" (2.03)

3.040* (5.24)

—

—

—

—

0.59

228

yes

* a, Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* F-DUMMY1 is F-UN>0.60; F-DUMMY2 is 0.60>F-UN>0.30; F-DUMMY2 is 0.30>F-UN.

102

TABLE 4.19 OLS Results of PCM Model with Industry

Unionization Dummies, 1986-88 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

I-DUMMY1

I-DUMMY2

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINVA/IS

IGROWTH

R 2

N

Industry Dummies

(1)

0.140* (10.42)

-0.023 (1.46)

-0.019* (3.42)

0.058* (9.86)

0.064* (5.77)

0.064 (1.46)

0.054 (0.71)

0.031* (4.23)

-0.011 (0.58)

0.089* (4.12)

0.52

204

no

(2)

0.130* (7.06)

-0.029 (1.53)

-0.020* (3.00)

0.048* (7.30)

0.001 (1.26)

—

—

0.035* (5.06)

—

0.004" (2.24)

0.33

305

no

(3)

0.098* (5.93)

0.004 (0.41)

-0.005 (0.72)

0.031* (4.30)

0.001 (1.29)

—

—

0.036* (5.39)

—

0.002 (1.40)

0.48

305

yes

* *Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* I-DUMMY1 is l-UN>0.60; I-DUMMY2 is 0.60>I-UN>0.30; I-DUMMY3 is 0.30>I-UN.

103

shows a statistically significant negative effect of only medium unionization on

PCM, it is observed that the effect of medium unionization is smaller than that

of high unionization. From the first specification, the estimated coefficient is

found to be -0.023 for I-DUMMY1, -0.019 for I-DUMMY2. All other explanatory

variables except intangible asset measures (ADVES/S and RDES/S) and

IINV/VIS have a positive and statistically significant impact on PCM.

Similarly from Tables 4.20 and 4.21 for both ROI and EV models, the

spline function indicates a strong negative union effect for firms in where the

industry union coverage ratio is between 30 and 60 percent, and then indicates

a weak significant slope thereafter. The estimated coefficient of I-DUMMY2 in

the specification (1) is -0.015 and -0.141 for ROI and EV models, respectively.

These results imply that unions in the firms in where the industry union

coverage ratio is between 30 percent and 60 percent capture the major portion

of ROI and EV. The impact of all other explanatory variables on ROI and EV

is found to be qualitatively and quantitatively similar to those in Tables 4.11

and 4.12.

4.2.1.4 Test Results for Sources of Union Gains

Evidence that research and development intensity still provides an important

source for union gains remains strong in all three models.''^ From Table 22,

^^0 test whether research and development intensity is an important source of union gains, three profit measures which are PCM, ROI and EV are separately estimated as a function of only F-UN, RDES/S and F-UN*RDES/S.

104

TABLE 4.20 OLS Results of ROI Model with industry

Unionization Dummies, 1986-88 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

I-DUMMY1

I-DUMMY2

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINV/VIS

IGROWTH

R2

N

Industry Dummies

(1)

0.138* (8.90)

0.013 (0.99)

-0.015" (2.17)

0.031* (5.00)

0.056* (3.29)

0.226* (4.37)

0.042 (0.42)

0.021" (2.39)

-0.020 (0.91)

0.074" (2.46)

0.29

204

no

(2)

0.125* (7.98)

0.022 (1.17)

-0.013" (2.33)

0.022* (3.53)

0.082* (4.37)

0.259* (5.35)

—

0.018" (2.34)

0.011* (6.03)

0.24

258

no

(3)

0.071* (4.51)

-0.016 (0.19)

-0.012 (1.31)

-0.020 (1.13)

0.071* (3.70)

0.150 (1.47)

—

0.021* (2.86)

—

0.004" (2.10)

0.46

258

yes

* *Significabt at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

* I-DUMMY1 is l-UN>0.60; I-DUMMY2 is 0.60>I-UN>0.30; I-DUMMY3 is 0.30>I-UN.

105

TABLE 4.21 OLS Results of EV Model with Industry

Unionization Dummies, 1986-88 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

I-DUMMY1

I-DUMMY2

GROWTH

ADVES/S

RDES/S

FS

IINV/VIS

IGROWTH

R

N

Industry Dummies

(1)

0.049 (0.90)

0.103* (1.50)

-0.141* (4.61)

0.129" (2.23)

3.271* (10.3)

-0.052 (0.07)

0.099* (2.45)

0.035 (0.49)

0.027 (0.22)

0.49

176

no

(2)

0.008 (0.14)

0.091* (1.46)

-0.095 (2.61)

0.159" (2.33)

3.832* (10.3)

—

0.106* (3.09)

—

—

0.49

228

no

(3)

-0.024 (0.35)

0.059 (0.79)

-0.090' (1.85)

0.128" (2.02)

3.139* (5.26)

—

0.084' (2.71)

0.60

228

yes

• a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level. I-DUMMY1 is l-UN>0.60; I-DUMMY2 is 0.60>I-UN>0.30; I-DUMMY3 is 0.30>I-UN.

106

TABLE 4.22 Test Results for Source of Union Gains for PCM, ROI and EV Models, 1986-88

(t-Statistics in Parentheses)

Independent Variables

CONSTANT

F-UN

RDES/S

F-UN*RDES/S

R2

N

PCM

0.144* (21.7)

-0.025 (1.39)

0.149' (1.69)

-1.404* (3.29)

0.13

243

ROI

0.153* (24.1)

-0.016 (0.82)

0.231" (2.24)

-1.624* (3.52)

0.15

243

EV

0.232* (5.11)

-0.019 (0.16)

2.415* (2.77)

-11.06* (3.88)

0.20

212

•k a Significant at the 0.01 level; "Significant at the 0.05 level; Significant at the 0.10 level.

107

the coefficients on the interaction term of F-UN*RDES/S are negative and

statistically significant at least at the 1 percent level. No surprisingly, the

estimated coefficients of F-UN for all three profit measures become statistically

insignificant when the interaction of F-UN*RDES/S is added to specifications

because the significant part of union coverage effect on profitability is captured

by this interaction term. The results are consistent with those of Hirsch (1990),

Becker and Olson (1992) and others."* Hirsch (1990) provided evidence that

sources of union gains did not result from output market structure (market

concentration rate, market share of the firm). In his study, he found that the

interaction term of the research and development intensity and firm unionization

measure had a negative and statistically significant impact on both Tobin's q

and ROI. In the same study, he also reported that the interaction term between

the firm unionization measure and firm market share had a positive end

significant effect on both profit measures. Hirsch's later finding supported the

finding of Clark (1984) which states that unions reduced profits only among

those businesses with small market shares even though Clark used completely

different data source from Hirsch's. Surprisingly, the finding of a positive and

significant estimated coefficient on the interaction term between unionization

and market share was criticized by Hirsch and Connolly (1987), Freeman

^ For the extensive survey on sources of union gains, see Hirsch and Addision (1986, p. 214) and Addision and Hirsch (1989, pp. 92-95).

108

(1983).'^ At that time, Hirsch and Connolly (1987) and Freeman (1983)

argued that Clark's argument and finding on this issue was specific to the PIMS

data.

4.2.1.5 Summary Assessment for the OLS Estimation Results over the 1986-88 Period

in this subsection, a framework is developed for analyzing the effect of

unionization on PCM, ROI and EV over the 1986-88 period by using OLS after

applying the White correction to error terms of all specifications. As mentioned

eariier, the analysis again provides clear evidence that unionized firms earn

substantially lower return than nonunionized firms. From specification (1)

reported in Tables 4.13-4.15, for fully unionized firms relative to lowly unionized

firms, PCM is lower by 7.1 percent, ROI by 7.2 percent and EV by 24.3

percent. The size of this negative effect is comparable to the size found by a

number of previous studies which are also used the sample data to cover more

than one year. By using aggregate data for manufacturing industries and

evaluating his findings at the mean of a dependent variable, not at the mean of

both dependent and independent variables. Freeman (1983) found that unions

reduce PCM by about from 39 percent to 44 percent for the Internal Revenue

^ Even though the measure on market share of firm is weak measure, when the models are estimated with interaction term between F-UN and MSF, significant and positive coefficients for this interaction term for all three models are found, which confirms the finding of Clark (these results are not shown on the table).

109

Service sample, by about from 13 percent to 19 percent for the Annual Survey

Manufactures sample. On the other hand, using Japanese manufacturing firms

as a sample and applying OLS technique, Brunello (1992) found that PCM was

lower by 36.5 percent and also ROI by 19.5 percent for fully unionized firms.

Hirsch (1991a) used firm level data and estimated that the ROI was 9.2 percent

lower in unionized firms than in nonunionized firms (at the mean of both

dependent and independent variables). Using micro data Becker and Olson

(1992) also found that EV and PCM were lower by 18 percent and 30 percent

(at the mean of the dependent variable), respectively.

On the other hand, only RDES/S among the explanatory variables in this

subsection experiences the mixed figure as in the first section of this chapter.

As in the first two specifications of Table 4.13, the estimated coefficients of

RDES/S are positive and statistically significant at least at the 5 percent level,

suggesting the significant effect of RDES/S on PCM measure. However, it is

found that the effect of RDES/S on both ROI and EV measures is insignificant,

which is also consistent with the results of the cross-section estimation.

As seen from Tables 4.16-4.21, when F-DUMMY and l-DUMMY which

are entered to the specifications separately, it is evident that the basic results

from PCM, ROI and EV are little affected by the new form of the firm

unionization measures. However, some interesting patterns emerged In the

comparisons of the coefficient estimates of each measure. According to the

estimated coefficients of both F-DUMMYs for all three models, the important

110

share of the union gain is distributed over the range of between 30 percent and

60 percent of the unionization. This result is partly consistent with that of

Hirsch (1991a) but inconsistent with the result of Becker and Olson (1992).

Hirsch (1991a) found that important portion of union gains occurs across 30

percent and over percent of unionization in Tobin's q equation, whereas

unionization had statistically significant impact on the rate of return on capital in

all ranges of unionized percentage. On the other hand, Becker and Olson

found that the union gains occur over the first 30 percent of unionization, not

over the first 60 percent.

In addition, the estimated coefficients of industry union coverage

dummies, l-DUMMYs, in all regression specifications also suggest that the

notable portion of union gains occurs over the range of between 30 and 60

percent of unionization. The findings from all specifications with industry

unionization dummies are in conflict with the findings of Becker and Olson

(1992). In their study, they found statistically insignificant coefficient estimates

for all of F-DUMMYs. The difference between these results and Becker and

Olson's, who obtained the industry union measure used from Kokkelenberg and

Sockell (1985), could be attributed to the source of data for the industry union

variable. On the other hand, even though Hirsch (1991a) used the same data

source for l-UN as does this study, comparable results for I-DUMMY1,

I-DUMMY2 and I-DUMMY3 do not occur, since in his study, he only considered

the effects of industry union coverage ratio, l-UN, but did not consider the

I l l

effects of the industry union coverage dummies (I-DUMMY1, I-DUMMY2 and

I-DUMMY3).

In general, all findings on the union-profit relation from all three models

under the cross-sectional data are qualitatively and quantitatively repeated in

this subsection. Again, all three models suggest that the negative and

significant union effect on profitability occurs for the firms that are moderately

unionized. Similarly, when l-DUMMYs are included in the specifications of all

three models, the above conclusion was repeated: a negative and significant

union effect on profitability for the firms whose Industry union coverage ratio are

between 30 and 60 percent. In addition, the comparison of cross-section

estimates of PCM, ROI and EV models to the estimates of all three models

over the 1986-88 period provides evidence that the number of explanatory

variables which has significant impact on PCM and ROI during 1986-88 is

greater than that during 1988.

4.2.2 The Results of the Fixed-Effect Regressions over the 1986-1988 Period

In this second subsection, the results of PCM, ROI and EV models under

fixed-effect technique over the 1986-88 period are discussed. Unfortunately,

the estimated coefficients of all three models under the fixed-effect technique

with individual effects are statistically insignificant. The insignificant coefficients

of all are found for all explanatory variables because large degrees of freedom

112

are lost when the firm means are removed. ^ On the other hand, when

regression equations are estimated with time-effects, the coefficients of most of

the explanatory variables are statistically significant at least at the 10 percent

level. '* Therefore, only the estimations of all three models under the

fixed-effect technique with time effects are presented. Here, to correct

heteroscedastic error terms no method is applied because one way to deal with

this problem is to use the fixed-effect technique itself.""

Additionally in this subsection three models are estimated with only firm

union coverage ratio."" The estimation results of all PCM, ROI and EV

models under the fixed-effect technique are shown in Tables 4.23-4.25,

respectively. Specification (1) of each table presents the estimation results of

each of three models with F-UN and the rest of the explanatory variables

whereas specification (2) reports the estimation results of the same model with

F-UN and some of explanatory variables which remain after stepwise

^ Under the fixed-effect technique with individual firm effects, the degrees of freedom of the sample is equal to the number of observations minus number of explanatory variables minus number of firms.

" Under the fixed-effect technique with time effects, the degrees of the freedom of the sample is equal to the number of observations minus number of explanatory variables minus number of periods (t=3).

^ For more information on this topic see Hsiao (1991).

^ Under the fixed-effect technique, dummies which are alternative measures of firm union coverage ratio cannot be used because the changes in firm profitability measures, PCM, ROI and EV are estimated as a function of changes in union coverage measure (F-UN) and in the rest of explanatory variables.

113

procedure. Also, in this section the estimation results of the PCM, ROI and EV

models with industry union coverage ratio are unreported since there were not

any statistically significant coefficient estimates of this variable for all three

models.

4.2.2.1 The Estimation Results of the PCM. ROI and EV Models with Firm Union Coverage Ratio under the Fixed-Effect Technigue

According to the coefficient estimates of F-UN, all three models strongly

support the hypothesis of negative and strong union effect on profitability. The

estimated coefficients of F-UN in both specifications of all three models are

found to be negative and statistically significant at the 1 percent level. More

specifically the coefficient estimate of F-UN is -0.048 for PCM model, -0.054 for

ROI model and -0.29 for EV model. Evaluated at the mean level, these

coefficients indicate that PCM, ROI and EV for a fully unionized firm relative to

a nonunionized, are reduced by 8.6, 8.4 and 30 percent, respectively.

Furthermore, all other explanatory variables except GROWTH and

IINV/VIS are found to have positive and statistically significant effect on PCM.

From specification (1), the estimated coefficient of FCAP/S is 0.036. Moreover

the estimated coefficients of FS and IGROWTH in the first specification are

0.031 and 0.104, respectively.

A comparison of the OLS and fixed-effect regression results for the PCM

model indicates that both ADVES/S and RDES/S become statistically more

114

TABLE 4.23 Fixed-Effect Results of PCM Model with

Firm Unionization, 1986-88 (t-Statistics in Parentheses)

Independent Variables

F-UN

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINVA/IS

IGROWTH

R

N

(1)

-0.048* (4.12)

0.036* (3.52)

-0.001 (1.19)

0.066' (1.69)

0.148" (1.90)

0.031* (3.87)

-0.035 (1.21)

0.104* (3.59)

0.36

201

(2)

-0.048* (4.06)

0.037* (3.59)

—

0.070' (1.76)

0.155" (1.96)

0.032* (3.94)

-0.037 (1.21)

0.057* (2.82)

0.35

201

* ai Significant at the 0.01 level. "Significant at the 0.05 level. 'Significant at the 0.10 level.

115

TABLE 4.24 Fixed-Effect Results of ROI Model with

Firm Unionization, 1986-88 (t-Statistics in Parentheses)

Independent Variables

F-UN

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINV/VIS

IGROWTH

R 2

N

(1)

-0.054* (3.47)

0.005 (0.69)

-0.001 (1.45)

0.196* (3.53)

0.152" (1.84) .

0.016' (1.81)

-0.055' (1.73)

0.106* (3.20)

0.24

201

(2)

-0.054* (3.47)

—

—

0.191* (3.50)

0.163" (1.98)

0.016' (1.75)

-0.065' (1.85)

0.056" (2.28)

0.23

201

* a Significant at the 0.01 level. "Significant at the 0.05 level. 'Significant at the 0.10 level.

116

TABLE 4.25 Fixed-Effect Results of EV Model with

Firm Unionization, 1986-88 (t-Statistics in parentheses)

Independent Variables

F-UN

GROWTH

ADVES/S

RDES/S

FS

IINV/VIS

IGROWTH

R

N

(1)

-0.298* (4.50)

0.001 (0.01)

3.570* (9.46)

0.032 (0.09)

0.068 (1.41)

0.013 (0.14)

-0.092 (0.59)

0.49

183

(2)

-0.290* (4.79)

—

3.604* (10.0)

—

0.060 (1.49)

0.47

228

* a Significant at the 0.01 level. "Significant at the 0.05 level. 'Significant at the 0.10 level.

117

significant and more Important determinants of the firm profitability under this

technique. From specification (1), the estimated coefficient is 0.066 for

ADVES/S and 0.148 for RDES/S.

For the ROI model, besides F-UN, the estimated coefficients of all other

explanatory variables except FCAP/S and GROWTH are found to be

significantly different from zero. Once more, both intangible capital measures,

ADVES/S and RDES/S become statistically more significant and more notable

determinants of ROI under the fixed-effect technique and estimated coefficients

in specification (1) are found to be 0.196 for ADVES/S and 0.152 for RDES/S.

While the estimated coefficient of FS in specification (1) is 0.016, the estimated

coefficient of IGROWTH is 0.106. On the other hand, the value of industry

inventory, IINV/VIS, is found to have a negative and statistically significant

impact on ROI, and the estimated coefficient of this variable in specification (1)

is -0.065.

Finally, in both specifications of EV model, in addition to the firm union

coverage variable, only ADVES/S among all explanatory variables has a

positive and statistically significant estimated coefficient and the coefficient of

ADVES/S in the first specification is estimated to be 3.570.

118

4.2.2.2 Summary Assessment for the Estimation Results of all PCM. ROI and EV Models under the Fixed-Effect Technigue

Using panel data, comparison of the OLS and fixed-effect estimates

reveals some changes in the results for all three models. The estimated

coefficients of F-UN for all three models under both the OLS and fixed-effect

techniques are similar in terms of sign, but differ in terms of magnitude and

significance level. The estimated coefficients of F-UN, ADVES/S, FS and

IINV/VIS for all three models under the fixed-effect technique are larger and

more significant than those under the OLS technique. Also, the coefficient

estimates of RDES/S for the both PCM and ROI models under the fixed-effect

technique are larger than those under the OLS technique.

Perhaps the most interesting additional information included in the fixed-

effect results for PCM and ROI models is the negative, insignificant and small

coefficients of the GROWTH variable and unstable coefficients of IGROWTH

from one specification to another. Actually, this unexpected and unstable

behavior of the coefficient estimates signifies that both GROWTH and

IGROWTH measures are not appropriate to explain the profitability under the

fixed-effect technique. First of all, both measures are constructed by taking

percentage changes of both variables, that is, they are already once

differenced. Since the fixed-effect technique differences all variables once

more, then the GROWTH and IGROWTH variables actually represent second

difference of sales and value of Industry shipments, respectively. Undoubtedly,

119

the second difference caused both variables to lose significant information on

the relationship between profitability and GROWTH (or IGROWTH). Therefore,

the result on the relationship of GROWTH (or IGROWTH) and profitability under

the fixed-effect technique cannot be reliable. From all findings in this section, it

can be concluded that the results of all models under the fixed-effects

technique seem to be more robust than OLS results because the fixed-effect

technique provides the possibility to capture the effects of omitted and

unobservable variables which are related to both firm profits (especially PCM

and ROI) and other explanatory variables (especially F-UN, ADVES/S, RDES/S,

FS and IINVA/IS).

4.2.3 The Results of the Random-Effect Regressions

over the 1986-88 Period

The results of PCM, ROI and EV models under the random-effect

technique are pursued in Tables 4.26-4.28, respectively, in this third subsection.

In this technique the same specifications are also used for all three model as in

the previous part of this chapter. While the estimates of all three models with

all explanatory variables are shown in specification (1), the estimates of the

same models with some of independent variables which remain after the

stepwise estimation procedure are presented in specification (2). The final

specifications in those tables explore the estimates of the 16 two-digit industry

dummies and explanatory variables which are already used in the second

specification. Here again, we did not apply any method to correct

120

heteroscedastic error terms since the random-effect technique itself corrects for

this problem.

4.2.3.1 The Estimation Results of the PCM. ROI and EV Models with Firm Union Coverage Ratio under the Random-Effect Technigue

Once more, in all specifications of all three models under the

random-effect technique, the estimated coefficients on the F-UN variable for all

PCM, ROI and EV models still remain negative and statistically significant at the

1 percent level. From the first specification of Tables 4.26-4.28, PCM is

reduced by 8.0 percent, ROI by 7.3 percent and EV by 29 percent when firm

union coverage, F-UN, is increased by 100 percent."*^

As seen from the first specification for the PCM model, while the

coefficient estimates of FCAP/S, GROWTH, FS and IGROWTH are all positive

and significant at least at the 5 percent level, the coefficients of ADVES/S,

RDES/S and IINV/VIS are not significantly different from zero at least at the 10

percent level. From the first specification of Table 4.26, the coefficient is

estimated to be 0.040 for FCAP/S, 0.029 for GROWTH, 0.028 for FS and 0.127

for IGROWTH. The comparison of the estimation results of PCM model under

the fixed-effect technique with those under the random-effect technique reveals

^^When F-DUMMYs and l-DUMMYs are employed rather than union coverage ratio under the random-effect technique, the similar result are found as under the OLS; the significant portion of union gains occurred over 30-60 percent of unionization.

121

TABLE 4.26 Random-Effect Results of PCM Model with

Firm Unionization, 1986-88 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

F-UN

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R 2

N

Industry Dummies

(1)

0.128* (9.04)

-0.045* (3.38)

0.040* (5.79)

0.029* (3.52)

0.076 (1.27)

0.101 (1.38)

0.028* (3.87)

-0.024 (1.06)

0.127* (4.28)

16.59

0.40

201

no

(2)

0.145* (10.7)

-0.047* (4.02)

0.047* (9.05)

0.030" (2.58)

—

0.134' (1.93)

0.028* (4.00)

—

0.109* (4.26)

24.79

0.39

236

no

(3)

0.156* (7.97)

-0.039 (2.63)

0.048* (6.93)

0.015' (1.73)

—

0.045 (0.70)

0.029* (4.27)

—

0.065* (2.57)

14.16

0.58

236

yes

* *Significant at the 0.01 level; "Significant at the 0.05 level;'Significant at the 0.10 level.

122

TABLE 4.27 Random-Effects Results of ROI Model with

Firm Unionization, 1986-88 (t-Statistics in Parentheses)

Independent Variables

CONSTANT

F-UN

FCAP/S

GROWTH

ADVES/S

RDES/S

FS

IINVA/IS

IGROWTH

F-Statistics

R

N

Industry Dummies

(1)

0.147* (9.15)

-0.047* (2.87)

0.013' (1.69)

0.058* (4.52)

0.210* (3.09)

0.091 (1.10)

0.015' (1.79)

-0.051" (2.04)

0.024 (0.71)

11.03

0.31

201

no

(2)

0.172* (12.1)

-0.052* (4.19)

0.014" (2.15)

0.051* (4.58)

0.167" (2.47)

—

0.014'' (2.12)

-0.065' (2.67)

14.30

0.25

258

no

(3)

0.097* (5.27)

-0.043" (2.38)

-0.024 (1.33)

0.041* (3.81)

0.149" (2.11)

—

0.019" (2.50)

-0.037 (1.55)

—

8.21

0.42

258

yes

* a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

TABLE 4.28 Random-Effect Results of EV Model with

Firm Unionization, 1986-88 (t-Statistics in Parentheses)

123

Independent Variables

CONSTANT

F-UN

GROWTH

ADVES/S

RDES/S

FS

IINV/VIS

IGROWTH

F-Statistics

R

N

Industry Dummies

(1)

0.148' (1.93)

-0.281* (3.41)

0.120' (1.90)

3.533* (10.2)

-0.127 (0.30)

0.068 (1.58)

-0.016 (0.14)

-0.147 (0.82)

25.4

0.50

183

no

(2)

0.229* (8.17)

-0.289* (4.65)

0.096' (1.87)

3.641* (11.7)

—

—

—

—

68.80

0.47

228

no

(3)

0.100* (3.00)

-0.174" (2.45)

0.073 (1.58)

3.339* (11.3)

—

—

—

—

22.75

0.66

228

yes

* a Significant at the 0.01 level; "Significant at the 0.05 level; 'Significant at the 0.10 level.

124

that the intangible capital measures, ADVES/S and RDES/S, become important

determinants of PCM under the fixed-effect technique, but not under the

random-effect technique.

From the first two specifications of Table 4.27, besides F-UN, FCAP/S,

GROWTH, ADVES/S, FS and IINV/VIS among all explanatory variables are

found to have a statistically significant impact on ROI. As pursued in the first

specification, the estimated coefficient was found to be 0.013 for FCAP/S, 0.058

for GROWTH, 0.210 for ADVES/S, 0.015 for FS and -0.051 for IINV/VIS.

Again, no significant effect of RDES/S on ROI is found.

On the other hand, from Table 4.28, besides unionization only

GROWTH and ADVES/S have a positive and statistically significant impact on

EV. The estimated coefficients of GROWTH and ADVES/S in specification (1)

are 0.120 and 3.533, respectively.

4.2.3.2 Summary Assessment for the Random-Effect Regressions

Once more, all three models under the random-effect technique

supported the proposition of a negative and significant union effect on

profitability. A comparison of the random-effect and fixed-effect estimates

reveals no significant qualitative and quantitative change in supporting the

hypothesis of negative union effect on firm profitability. However, when both

techniques are compared in terms of the magnitudes of the estimated

coefficients from one specification to another, the coefficient estimates under

125

the fixed-effect technique are more stable than those under the random-effect

technique. On the other hand, the fixed-effect technique suggests a positive

and significant effect of stock value of research and development on both PCM

and ROI measures while the random-effect technique indicates that the effect of

that variable on both profitability measures is not statistically different from zero.

CHAPTER V

SUMMARY AND CONCLUSION

Since the 1970s, unionization in the manufacturing sector of the United

States has begun to decline substantially. With this substantial decline, the

literature on labor and industrial economics has shifted its focus on the

relationship between unionization and profitability. Although in the literature, the

profitability in terms of its determinants has been theoretically investigated for a

long time, the formal empirical investigation on the effects of unions on

profitability starts only at the beginning of 1980.

The pioneering studies of Freeman (1983) and Clark (1984) in this

specific literature were the first to attempt to investigate the effects of unions on

profitability. By using a sample of industry level data for the period 1958-1976,

Freeman (1983) concluded that there was a negative and significant union

effect on both price-cost margin and the ratio of quasi-rents to capital during the

underlying period. Just afterward, the findings of Clark (1984), which come

from the use of a sample of North American product-line business data, and the

use of rate of returns on sales and capital as a measure of profitability,

supported Freeman's conclusion.

After these two influential empirical studies, an explosive research on the

union-profitability effect has developed. In general, there has been agreement

126

127

on the negative union effect on profitability despite the sample of data, the

measure of profitability, and the measure of unionization which differ from

studies to studies. But, the dispute pertaining to the union-profit effect is

ongoing as to the size of the negative union effect on the profitability,

depending on the measure of profitability, the estimation technique, the

measure of unionization and alternative measures of unionization since the

effect of unionism on profitability differs depending on the sample of data, the

measure of profitability, the measure of unionization and the estimation

technique used.

In addition, the estimated union effect on profitability in the previous

studies would be biased since these studies could not specify their profitability

equations so as to capture the possible effects of omitted or mismeasured firm

specific, industry specific and/or economy wide variables which exhibit variation

across firms and/or through time, and also correlated with firm unionization and

profitability.

In the light of the shortcomings of the existing literature on the

union-profit effect, the main objective of this study was to analyze statistically

and comprehensively the effect of unionism on firm profitability and the

sensitivity of such effect to different estimation techniques which were the OLS,

fixed-effect and random-effect techniques; to different profitability measures

which were PCM, ROI and EV; to different unionization measures which were

firm and industry union coverage ratios; and to alternative measures of

128

unionization which were union dummies and coverage ratio. In addition to the

main objective, the secondary objective of the study was to examine whether

the returns of research and development investment were still a major source of

union gains.

The fundamental analytical tools being used to carry out the stated

objectives were ordinary least squares, fixed-effect and random-effect

techniques. In the presence of heteroscedastic error terms under OLS

technique, the White correction approach was employed to correct the residual

terms. Finally, the investigation for any endogeneity between unionization and

profitability was carried out by using the Hausman exogeneity test procedure.

5.1 Summary of Results

In this study the effect of unionization on firm profitability was

investigated in some detail. By any of three profit measures, union measures

and estimation techniques, the effect of unionization on each was found to be

negative and significant, even though unionization declined significantly on

average in the firms which were in the private sector for the last ten years.

Regarding the union effect on profit measures, comparing three

estimation techniques in terms of the effect of unionization on profitability

measures, the effect which was estimated under the fixed-effect technique

differed from that under both the OLS and random-effect techniques, implying

129

that some unobservable or omitted variables which were related to both

profitability and unionization measures existed.

Also, the size of the effect of union coverage on profitability differed

depending on profit measures. Comparing the effect of firm unionization on

both PCM and ROI with that on EV, such effect on the long-run profitability

measure was greater than that on short-run measures. For example, when

excess value is proxied to firm profits, the findings from Tables 4.23-4.25

indicated that profitability for a fully unionized relative to a nonunionized firm

was reduced by 30 percent. In the case of price-cost margin, the results

suggested that profitability for a fully unionized firm relative to a nonunionized

firm was reduced by 8.6 percent. On the other hand, a comparison of the

union effect on ROI with that on PCM indicated no significant difference

between two short-run measures regarding the unionization effect.

From the findings of this study, it was also observed that the effect of

unionization on each profit measure differed depending on the union measures.

When the firm union coverage ratio was proxied for firm unionization, all three

models supported the proposition of negative union effect on firm profitability

regardless of which measures were used as a proxy for profitability. On the

contrary, all models provided no evidence of a negative union effect on

profitability when the industry union coverage ratio was proxied for firm

unionization. On the other hand, when both the firm and the industry union

coverage dummies were proxied for firm unionization, the findings from both

130

alternative measures showed no significant change. For example, the

estimation results from both measures provided strong evidence on the

negative union effect on profitability. Also, according to the estimation of both

measures, the spline function of both firm union coverage and industry union

coverage provided evidence that a significant portion of union gains existed

especially over the range of between 30 and 60 percent of unionization.

Finally, the estimations throughout the 1986-88 period showed that

research and development investment was still a major source of union gains in

spite of the fact that the investment on research and development has declined

in recent years.

5.2 Conclusion

Findings of this research on a sample of large U.S. manufacturing firms

for the 1986-1988 period provide strong support for the hypothesis of negative

union effect on profitability. Despite differences among analysis in

methodology, measures of profitability and unionization, time period, data

sources and unit of observations, the findings in this study are consistent with

most of previous studies in terms of supporting the hypothesis of a negative

union effect on profitability. The main conclusion of this study is that the effect

of unionization on firm profitability is negative and substantial regardless of

different estimation techniques, different profit measures and different

unionization measures. This study also concludes that the size of the negative

131

union effect differs depending on the estimation techniques, measures of

profitability and unionization measures as well as the alternative measures of

unionization.

Most importantly, the union effect on profitability differs depending on the

estimation technique, although the negative effect is found in all three

estimation techniques; the effect estimated by the fixed-effect technique is

found to be larger than that estimated by both the OLS and the random-effect

techniques. Theoretically, the existence of the difference between the size of

the effects estimated by the OLS and the fixed-effect techniques is an indication

of the existence of the omitted and/or unobserved variables such as worker

quality which are correlated with included explanatory variables such as union

status in profitability equations. Even though random-effect technique captures

individual and/or time effects in error terms, there is no justification for treating

individual effects and/or time effects as uncorrelated with other included

explanatory variables, as is assumed in the random-effect technique. The

random-effect approach may, therefore, suffer from the inconsistency due to the

omitted variables. In the light of the above explanation, the fixed-effect

technique is preferable to any alternative techniques used in this study.

The magnitude of the negative union effect on profit also differs

depending on the profitability measures. In this study, the size of the effect

under long-run profitability measure such as EV is found to be significantly

larger than that under the short-run measure such as PCM or ROI. This

132

difference indicates that investors expect the union to have a detrimental effect

on firm growth and future earnings. Theoretically, use of long-run profit

measures is preferable to short-run profitability measures because of the

dynamic characteristics of long-run profit measures. On the other hand, since

excess value incorporates businesses' expectations about unionism's effect on

future profits , it has an unknown degree of error. Then, empirically, the union

effect on profitability can be overestimated or underestimated. By taking into

account both theoretical advantage and empirical disadvantage of EV, and

empirical advantage and theoretical disadvantage of short-run measures such

as PCM or ROI, this study is indecisive on the preference of profitability

measures.

Finally, the union effect on profitability differs depending on unionization

measures; that is, the effect under the firm union coverage ratio is negative and

significant while it is insignificant under industry union coverage ratio. On the

other hand, the effect under the dummy variables, regardless of which union

measures are used to construct them, is negative and significant only for the

firms whose the underlying union coverage ratio is between 0.30 and 0.60. As

is understood from these findings, when the firm union coverage ratio is

compared to the industry union coverage ratio in terms of performance to test

the underlying hypothesis, this study shows that the firm union coverage ratio is

more favorable than the Industry union coverage. But, for the cases of the

dummy variables, this study is not conclusive on the choice between firm and

133

industry union coverage. Finally, our study suggests that the choice between

the firm union coverage ratio and the dummies constructed from such coverage

will depend on the purpose of the researcher; that is. If someone prefers to look

at the union effect on profitability only for those firms which have unionization

ratios between certain ranges, the dummy approach would be preferable to the

alternative one, but if the purpose is to find the effect of unionization on

profitability for all firms in the sample, the use of the coverage ratio will be

certainly favorable to the dummy approach.

In the light of the findings of this study, it is argued that in recent years,

the poor economic performance of the U.S. manufacturing firms is likely and

partially caused and mitigated by enhancing union organizing and bargaining

power of union, i In the absence of appropriate productivity increase, union

wage increases will likely continue reducing domestic and foreign

competitiveness of the U.S. manufacturing firms. Therefore, some

modifications in the U.S. labor law may be needed for reallocating bargaining

power between firm and union managements.

Even though this study is conclusive for all measures of profitability and

unionization except the alternative measures of unionization, and for all

estimation techniques, there are some limitations to this research. The first

one is the difficulty in obtaining the measure of firm union coverage ratio. Since

the firm union measure is not publicly accessible, this study was forced to

construct the firm union coverage ratio by aggregating the number of covered

134

workers across all of a firm's listed contracts from the publications of the

Bureau of Labor Statistics and by dividing that number by the total workers in a

firm from the Compustat tape. Matching two data sources in this way forced us

to choose large firms nonrandomly. So, the results cannot be easily

generalized to the whole U.S. manufacturing sector. Second, the endogeneity

of unionization with respect to profitability is examined subsequently in a less

satisfactory manner; that is, we are forced to use predicted value of

unionization as a proxy for the firm unionization because it is extremely difficult

to find the proxies which influence the union coverage ratio but not profitability.

For these reasons, it is recommended that further research on the

union-profit effect be done constructing firm union measures "over time" and

applying the techniques which could capture the possible effect of the

omitted/unobserved determinants of profitability which are related firm

unionization.

In addition to the estimation of the union effect on profitability under

fixed-effect technique, the major contribution of this study to the literature is the

fact that the findings with the alternative measures of unionization, profitability

and the alternative estimation techniques provide strong support for the

hypothesis of a negative union effect on profitability and also provides evidence

on how the size of the negative effect of unionism differs depending on the

measures of unionization, profitability and estimation techniques.

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APPENDIX

TABLE A.I Names and Industry Codes of the Firms

Name of the Firms 4-Digit Industry Codes

Philip Morris Cos. Inc. 2111 Quaker Oats Co. 2000 IBP Inc. 2011 Heinz (H.J) Co. 2030 Hershey Foods Corp. 2060 Anheuser-Busch Cos. Inc. 2082 Fieldcrest Cannon 2211 Georgia-Pacific Corp. 2430 Mead Corp. 2600 Weyerhaeuser Co. 2421 Boise Cascade Corp. 2621 Champion International Corp. 2621 Intl. Paper Co. 2631 Kimberly-Clark Corp. 2621 Potlatch Corp. 2621 Scott Paper Co. 2621 Union Camp. Corp. 2621 Westvaro Corp. 2621 Federal Paper Board Co. 2631 Longview Fibre Co. 2670 Minnesota Mining & MFG Co. 2670 New York Times Co. 2711 American Cyanamid Co. 2800 Dow Chemical 2800 FMC Corp. 2800 Olin Corp 2800 Goodrich (B.F) Co. 282 Hercules Inc. "^JT Union Carbide Corp. 2821 Johnson & Johnson 2834 Colgate-Palmolive Co. 2840 Procter & Gamble Co. 2840 PPG Industries Inc. 2851 Ashland Oil Inc. 291^ Atlantic Richfield Co. 291 ^ Chevron Corp. 291

143

144

TABLE A.I (Continued) Names and Industry Codes of the Firms

Name of the Firms 4-Digit Industry Codes

Exxon Corp. 2911 Mobil Corp. 2911 Phillips Petroleum Co. 2911 Sun Co. Inc. 2911 Texaco Inc. 2911 USX Corp. 2911 Goodyear Tire & Rubber Co. 3011 Armstrong World Inds. Inc. 3290 Lone Star Industries 3241 National-Standard Co. 3310 Worthington Industries 3310 Armco Inc. 3312 Bethlehem Steel Corp. 3312 Cyclops Industries Inc. 3310 Inland Steel Industries Inc. 3312 LTV Corp. 3312 Asarco Inc. 3330 Aluminum Co. of America 3334 Reynolds Metals Co. 3334 Stanley Works 3420 Briggs & Stratton 3510 Brunswick Corp. 3510 Cummins Engine 3510 Outboard Marine Corp. 3510 Deere & Co. ^^23 Ingersoll-Rand Co. 3560 Dresser Industries Inc. 3530 Timken Co. ^^^2 Interlake Corp. 3569 Tecumseh Products Co. 3585 Westinghouse Electric Corp. 3812 General Electric Co. 3600 Maytag Corp. 3630 Whirlpool Corp. ;^^;J^ Chrysler Corp. ^2? \ General Motors Corp. "^^l^ General Motors-Class H 3812 Navistar International 3711

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TABLE A.I (Continued) Names and Industry Codes of the Firms

Name of the Firms 4-Digit Industry Codes

Dana Corp. 3714 Smith (A.O) Corp. 3714 TRW Inc. 3760 Textron Inc. 3720 Boeing Co. 3721 General Dynamics Corp. 3721 McDonnell Dougles Corp. 3721 Teledyne Inc. 3724 United Technologies Corp. 3724 Rohr Industries 3728 Todd Shipyards Corp. 3730 Huffy Corp. 3751 Lockheed Corp. 3760 Martin Marietta Corp. 3760 Rockwell Intl. Corp. 3760 Litton Industries Inc. 3812 Loral Corp. 3812 Honeywell Inc. 3822 Fischer & Porter Co. 3823 Xerox Corp. 3861 Willamette Industries 2621 Acme Steel Co-Del 3312 Laclede Steel Co. 3312 Wyman-Gordon Co. 3460 Trico Products Corp. 3714 Emhart Corp. 3452 Cameron Iron Works 3533 Champion Spark Plug 3690 Owens Corning Fibrglas 3290 Unisys Corp. 3570

Note: This four-digit Standard Industry Classification (SIC) Codes designates the product or service contributing the largest percentage of company sales (Compustat, Section 10, pp. 3).