What Do Unions Do to Productivity? A Meta‐Analysis
ABSTRACT The impact of unions on productivity is explored using metaanalysis and metaregression analysis. It is shown that most of the variation in published results is due to specification differences between studies. After controlling for differences between studies, a negative association between unions and productivity is established for the United Kingdom, whereas a positive association is established for the United States in general and for U.S. manufacturing.

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Published by Blackwell Publishing, Inc., 350 Main Street, Malden, MA 02148, USA, and 9600 Garsington
Road, Oxford, OX4 2DQ, UK.
R
, Vol. 42, No. 4 (October 2003). © 2003 Regents of the University of California
650
What Do Unions Do to Productivity?
A MetaAnalysis
CHRISTOS DOUCOULIAGOS and PATRICE LAROCHE*
The impact of unions on productivity is explored using metaanalysis and
metaregression analysis. It is shown that most of the variation in published
results is due to specification differences between studies. After controlling for
differences between studies, a negative association between unions and produc
tivity is established for the United Kingdom, whereas a positive association is
established for the United States in general and for U.S. manufacturing.
T
HE
RELATIONSHIP
BETWEEN
UNIONS
AND
PRODUCTIVITY
has
attracted considerable attention from scholars in industrial relations and
economics, as well as from policymakers, unions, and business in general.
Despite voluminous theoretical literature, controversy continues regarding
the impact of unions on productivity, as well as on other aspects of busi
ness, such as employment, research and development (R&D), profitability,
and investment. In traditional economic analysis, unions are said to dis
tort labor market outcomes through, for example, legal and customdriven
restrictions on relative wages, the imposition of employment restrictions,
and protection against layoffs. Unions are said also to be a contributing
factor to aggregate as well as sectoral unemployment and the associated
output losses. In contrast to these arguments, Freeman (1976) and Freeman
and Medoff (1984) argued that unions can raise productivity by providing
workers with a means of expressing discontent as an alternative to “exiting,”
by opening up communication channels between workers and management,
and by inducing managers to alter methods of production and to adopt
more efficient policies.
The controversy in the theoretical literature is matched by controversy
in the empirical literature. Empirical findings are divided between positive
and negative unionproductivity effects, and many studies cannot reject the
hypothesis of a zero effect. Hence generalizations from the available evidence
* The authors’ affiliations are, respectively, School of Accounting, Economics and Finance, Deakin
University, Victoria, Australia, and Institut d’Administration des Entreprises, University of Nancy,
Nancy, France. Email:
douc@deakin.edu.au
and
patrice.laroche@univnancy2.fr.
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What Do Unions Do to Productivity? A MetaAnalysis
/ 651
are not obvious using traditional literature reviews. The aim of this article
is to make more lucid the relation between unions and productivity by using
metaanalysis and metaregression analysis. Metaanalysis is now used
widely to identify and quantify patterns, draw inferences from the diversity
of results, and detect possible regularities in the association between unions
and productivity. Metaanalysis is used to implement a quantitative syn
thesis of the available research and, where possible, to generalize from the
results derived from the numerous singular studies (Rosenthal 1984). Meta
analysis is a scientifically valid statistical procedure that has been developed
to quantify associations drawn from an existing body of literature (Wolf
1986; Hunter and Schmidt 1990).
The metaanalysis presented in this article involves a comprehensive
survey and quantitative analysis of the published empirical literature.
Metaregression analysis is used to examine the influence of methodologic
features and data differences on reported estimates of unionproductivity
effects. Additionally, we explore the notion, prevalent in the empirical liter
ature, of the existence of an invariant unionproductivity effect.
While theoretical developments focus on efficient bargains—bargaining
over wages and work practices as well as bargaining over wages and
employment—empirical analysis of the net impact of unions on productiv
ity remains of considerable interest. Hence metaanalysis of this empirical
literature is important. For example, policymakers have maintained their
interest in this area within the overall context of policy action and concerns
over labor market flexibility and labor market deregulation. Even though
the influence of unions has fallen and union membership has diminished in
most countries, industry in general continues to be concerned about the
impact of unionization, especially where productivity becomes important in
offsetting any adverse effect on cost competitiveness arising from the higher
wages of unionized labor. This has become even more imperative with the
rapid expansion of global markets. Furthermore, applied research in this
area continues, responding at least in part to these broader concerns. For
example, 37 studies estimating directly the impact of unions on productivity
were published in the 1990s compared with 31 published in the 1980s and
20 articles published in this area since 1995.
Previous Research and Reviews
There is now a sizable theoretical literature that explores both the hypo
thesized costs and the benefits of trade unions. Examples include Addison
(1982, 1985), Addison and Barnett (1982), Freeman and Medoff (1984),
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Kuhn (1985), Hirsch and Addison (1986), Turnbull (1991), and Belman (1992).
Details of the theoretical arguments can be found in these and other sources.
Conceptually, unionism can booster or hamper labor productivity.
Because of the ambiguity over the net effect of unions, most of the existing
studies begin their empirical investigation without presupposing a specific
direction, leaving the conclusion to empirical findings. However, a common
conceptual framework serves as a starting point for empirical investigation.
This conceptual framework is the socalled twofaces view of unionism
(Freeman and Medoff 1984): the monopoly face and the collective voice/
institutional response face.
The
monopoly face
of unionism refers to a number of adverse wage and
nonwage effects. One of the most well established effects of unions is the
ability to increase wages above competitive levels (Lewis 1963). Another
dimension is unfavorable effects on R&D spending and tangible and intang
ible investments. Union rentseeking acts as a tax on the return on invest
ment and limits innovative and investment activities (see Connolly, Hirsch,
and Hirschey 1986; Hirsch and Link 1987). These can have a detrimental
impact on the dynamic path of productivity.
Unions can have a direct negative impact on productivity by restricting
managerial discretion. For example, unions may force firms to adopt ineffi
cient personnel hiring and firing practices. Legal restrictions against layoffs
and closedshop arrangements have an impact on efficient factor usage
and hence productivity. Unions also may favor restrictive work practices,
curbing the pace of work, hours of work, and skill formation. They also may
obstruct the introduction of new technology (see McKersie and Klein 1983).
Productivity is affected also through strike activity. This arises through
working days lost, as well as noncooperative behavior that precedes or
follows strikes (see Flaherty 1987).
The other aspect of unions is the
collective voice and institutional response
face
(CV/IR) emphasized by Freeman and Medoff (1984). The CV/IR
model draws on the exitvoice dichotomy of Hirschman (1970). In this
framework: “voluntary quits become the labor market expression of exit,
and unions become the institution for the expression of (collective) voice”
(Turnbull 1991:137). By providing workers with a means of expressing dis
content at the workplace, unions can reduce the extent to which quits and
absenteeism lead to a suboptimal degree of labor turnover. By presenting
unions as an alternative to resignation and apathy, Harvard scholars
delivered an argument in favor of union representation. This channel is
important because high labor turnover can reduce productivity in a work
place through a direct loss of firmspecific training (see Addison and Barnett
1982; Freeman 1976).
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What Do Unions Do to Productivity? A MetaAnalysis
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According to Freeman and Medoff (1984), unions can enhance produc
tivity by improving communication between workers and management. The
opening of communications channels between management and workers
can result in integrative rather than distributive bargaining (Dworkin and
Ahlburg 1985). Unions may provide additional information to a firm about
the preferences of employees, thus permitting the firm to choose a better
mix among working conditions, workplace rules, and wage levels. These can
result in a more satisfied, cooperative, and productive workforce.
In addition, unions may be responsible for a “shock effect.” Unions can
induce managers to alter methods of production and adopt more efficient
personnel policies (Slichter, Healy, and Livernash 1960). Union activities
also may improve worker morale and motivation. The potentially arbitrary
nature of decisions such as promotions or layoffs can be reduced by the
presence of unions. Consequently, the employee is more likely to see his or
her employer as fair. Leibenstein (1966) emphasized that one of the major
areas for improving Xefficiency in the firm is worker morale and motiva
tion. Further, unions often stress seniority rules. There is a positive associ
ation between productivity and experience. Seniority rules exclude a system
of subjective selection, and the seniority system emphasizes ability and
merit. It also may reduce conflict between seniority and efficiency (Rees
1962).
The CV/IR approach offers new insights into the role of unions in labor
productivity. This framework is based on a theory that “may be interpreted
as a hypothesis that internal organization matters and as an extension of
modern organization theory which abandons the standard textbook neo
classical economic perception of the firm as a machine . . .” (Addison and
Barnett 1982:147).
The two faces of unions are not incompatible. Hirsch (1997:37) notes that
“The monopoly and collective voice faces of unionism operate sidebyside,
with the importance of each being very much determined by the legal and
economic environment in which unions and firms operate. For these rea
sons, an assessment of union’s effects on economic performance hinges on
empirical evidence.”
Union productivity effects have been studied in a variety of industries,
including construction, manufacturing, mining, and services, as well as the
public sector. Most of the studies use U.S. or U.K. data, with many con
flicting results reported. Unfortunately, the existing empirical studies do not
individually provide definitive answers on the relationship between unions
and productivity. These studies use disparate variables, methods, and
samples, and hence it is necessary to review the available studies and draw
inferences from them. Moreover, it is important to investigate the extent to
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which the differences between studies serve as potential explanations for the
disparity in the results across the studies. The differences between study
results may reflect actual differences in the relationship between unions and
productivity. The differences also could be due to the nature of the research
process.
There have been a number of reviews of this literature, some conducted
by leading experts. The conclusions drawn in the major reviews are broadly
similar. For example, in their review, Addison and Hirsch (1989:79) argued
that “Based on the extant evidence, we conclude that the average union
productivity effect is probably quite small and, indeed, is just as likely to be
negative as positive.” They note also that “. . . there is no compelling evi
dence that, in general, the net effect of unions on productivity is positive or
negative” (1989:83). In his review, Kuhn (1998:1048) concluded that “A fair
summary of the industry studies is that most estimates are positive, with
the negative effects largely confined to industries and periods known for
their conflictual unionmanagement relations, or to the public sector.” Sim
ilar conclusions that the evidence supports neither a negative nor a positive
relationship are drawn by many other authors (see, for example, Wilson
1995). Preempting the need for metaanalysis, in the course of her review,
Booth (1995:223) noted that it is “necessary to have evidence on the
union effects from a number of different studies before drawing any firm
conclusions.”
The problem with qualitative reviews of any literature is that they may
suffer from what Stanley (2001) calls “casual methodological speculation.”
Since they are qualitative, they are based usually on opinion, and con
clusions are drawn largely from subjective interpretation of the available
evidence, even when specialists conduct them. This makes qualitative reviews
prone to a greater degree of speculation than quantitative reviews. The
absence of statistical investigation of empirical results means that qualita
tive reviews lack formal testing of a hypothesis. While qualitative reviews
assist with the subjective assessment of a hypothesis, it is only through a
quantitative review that a contentious hypothesis can be tested formally.
In contrast to existing reviews, in this article we present the first
tative
review of the unionproductivity effects literature through meta
analysis. Metaanalysis is used to “summarize, evaluate and analyze empirical
economic research” (Stanley 2001:131). It is well know that methodologic
and data differences have an impact on empirical estimates. The issue is how
to quantify that impact. Metaanalysis is based on a pronounced examina
tion of differences in specification and datasets and is used to quantify the
impact these have on productivity effects. Metaanalysis helps to make sense
out of the substantial variation in unionproductivity estimates. It is very
quanti
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What Do Unions Do to Productivity? A MetaAnalysis
/ 655
difficult to evaluate the multidimensional nature of empirical investigations
using traditional literature reviews. For example, reviewers are forced to
assess the impact of specification differences without statistical tools that
enable them to identify the impact of specification differences after control
ling for, say, data differences.
Metaanalysis should be seen as complementary to traditional reviews, a
way to analyze estimates and explain the variation of interstudy differences
(see Espey 1998). Despite differences in the review process, we show in this
article that the conclusions drawn from the existing reviews are correct with
respect to the entire pool of evidence. However, we can draw different con
clusions about the direction of the productivity effects for important sub
samples of this literature. In particular, it is possible to conclude that unions
have a negative impact on productivity in the United Kingdom and Japan
and that unions have a positive impact on manufacturing in the United
States. Importantly, in contrast to some of the qualitative reviews, the avail
able evidence indicates that some of the productivity effects are of economic
significance.
MetaAnalysis and MetaRegression Analysis Methodology
Metaanalysis was developed to facilitate a
thesis. There is now a burgeoning reference literature on metaanalysis [see, for
example, Cook et al.
(1992); Hedges and Olkin (1985); Hunter and Schmidt
(1990); Wolf (1986)]. Stanley (2001) offers a recent review of the growing
list of applications of metaanalysis in economics.
There are four steps in metaanalysis. Metaanalysis involves identifica
tion and calculation of the association between variables of interest (known
as an
effect size
) by considering all the available relevant empirical literature.
Hence the first step in any metaanalysis is identification of the relevant
empirical literature. In the present study this is the published literature on
unionproductivity effects. The second step involves derivation of effect
sizes from each study or calculation of effect sizes from information provided
in each study. Two effect sizes are used in this article, the partial correlation
coefficients between unions and productivity and the unionproductivity
effects. The third step is calculation of summary statistics relating to the
effect sizes. The fourth step is moderator analysis—the identification of the
sources of variation between published effect sizes.
The most common approach known as
lation of summary statistics involving the associations of interest. The
key statistics of interest are the mean, the weighted mean, a measure of
quantitative
research syn
metaanalysis
involves the calcu
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homogeneity of research results, and confidence intervals constructed
around the mean. A separate branch known as
is used to uncover the sources of heterogeneity of research results. This
involves regressing study characteristics on the effect size derived from the
studies against a set of potential explanatory or moderator variables. Both
types of metaanalyses are presented in this article, with the emphasis on
metaregression analysis.
metaregression analysis
The Database.
database. We compiled all the published studies exploring the relationship
between unions and productivity. There are a number of unpublished
studies exploring this relationship, but these have not been included. Results
presented in unpublished material, such as manuscripts and working papers,
can change by the time they reach their published form, and hence in many
cases they may be less reliable than those found in published material.
An extensive computer search was conducted for studies written in Eng
lish or French. A total of 73 statistically independent studies was compiled,
and these studies are used in the metaanalysis and metaregression ana
lysis.
In metaanalysis, studies are regarded as statistically independent if
different authors produce them or when they are by the same author but
different samples are used (see Hunter and Schmidt 1990). There are actu
ally more than 73 published studies in this area. However, in some cases the
same authors have produced more than one published work using the same
dataset. These studies cannot be regarded as statistically independent. The
approach taken in this article was to average these nonstatistically inde
pendent studies and treat them as a single study. For example, Fitzroy and
Kraft (1987a, 1987b) use the same data, as do Guthrie (2001) and Guthrie,
Spell, and Nyamori
(2002).
From the point of view of metaanalysis, when two different authors use
the same dataset, both studies are regarded as statistically independent.
This is standard practice in metaanalytic reviews [see, for example, Espey,
Espey, and Shaw (1997) and Thiam, BraveUreta, and Rivas (2001)].
All the studies included in the metaanalysis provide direct measures of
the association between unions and productivity, with productivity as the
dependent variable and unionism as part of a set of explanatory variables.
The starting point for metaanalysis is compilation of the
1
2
1
Moreover, the quality of working papers varies. For example, those from the National Bureau of
Economic Research (NBER) are of a very high quality, and most of these have been published. How
ever, there are working papers from other centers that have remained unpublished after many years and
in some cases after decades. Nevertheless, it is the case that most of the working papers have been
published and are included in the metaanalysis presented in this article.
2
A number of databases were searched, including EconLit, Proquest/ABI Inform, and EBSCO.
Page 8
What Do Unions Do to Productivity? A MetaAnalysis
/ 657
A number of empirical studies were excluded from the metaanalysis. These
include (1) the extensive body of literature exploring the impact of unions
on wages, (2) studies that explore the links between unions and employ
ment, profitability and/or investment, (3) studies that explore the links
between unions and productivity but do not provide sufficient information
from which effect sizes could be calculated, (4) macroeconomic studies that
focus on the relationship between corporatist economic policies and eco
nomic performance, (5) studies that explore the association between labor
relations climate and productivity through strike activities, grievances pro
cedures, and quality of working life, (6) estimates of the unionproductivity
growth
effect,
and (7) studies using probit models because they are not
comparable with the included studies. A full list of excluded studies is avail
able from the authors.
3
Effect Sizes.
correlation coefficient, and from most of the studies we were able to collect
some information on the unionproductivity effect.
measures of the association between the variables of interest (the effect sizes).
That is, the focus of the metaanalysis is the partial correlation between
unionization and productivity, as well as the productivity effects. These
measure the strength and, importantly, the direction of association between
unionization and productivity while holding other factors constant. The
techniques developed for traditional forms of metaanalysis are based on
measures such as correlations. Metaregression analysis can be used for
both correlations and measures more favored by economists, such as elas
ticities. One of the benefits of analyzing partial correlations is that it facili
tates comparisons with other metaanalyses of workplace interventions and
performance. Examples where correlations are used include the metaanalysis
of job satisfaction and productivity (Miller and Monge 1986); absenteeism
and job performance (Bycio 1992); the impact of profit sharing, employee
share ownership, and employee participation in decision making (Doucouliagos
1995); and board of directors size and financial performance (Dalton et al. 1999).
From each of the published studies we calculated the partial
4
These are the preferred
3
Where studies reported results for both growth and levels, only the later was used.
None of the 73 studies reported partial correlation coefficients. However, they do report regression
coefficients, standard errors,
t
statistics, or levels of statistical significance. The partial correlation coef
ficients are calculated by using the
t
statistics reported in the primary studies. Where
reported, they can be calculated from the reported levels of statistical significance or from the reported
regression coefficients and standard errors. The formula used to calculate partial correlations is:
where
t
is the
t
statistic and
df
is degrees of freedom. Note that this will always produce a positive
number, so it is necessary to convert it to a negative number if the regression coefficient is negative (see
Greene 2000:Chap. 6).
4
t
statistics are not
,
ttdf
22
/( )
+
Page 9
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Most studies report more than one set of results. The approach adopted
in this article is to use only the results deemed by the study’s author or
authors as the preferred result. Hence we ignore any results undertaken for
sensitivity analysis, for exploration purposes, or just out of speculation.
Where studies report more than one valid regression coefficient, we aver
aged these—in some cases the weighted average was used when the same
author in the same article used different sample sizes.
MetaRegression Analysis
is to identify moderator variables and to explore the impact of specifica
tion on the estimated unionproductivity effect. Each of the 73 primary
studies used regression analysis to estimate unionproductivity effects.
Metaregression analysis is a regression analysis of the regression analysis
reported in the existing pool of studies. The published studies used a
number of different specifications, introduced different control variables,
and used different datasets covering different levels of aggregation, different
time periods, and different industries. Metaregression analysis can be used
to detect whether these study characteristics are associated in any way with
the estimated study outcomes. This enables a
impact of differences in research design, methodology, data, and estimation
on reported study outcomes that is not possible in a traditional narrative
and qualitative literature review.
Metaanalysis also can be used to identify moderator variables. However,
the advantage of metaregression analysis is its multivariate context. Meta
regression analysis allows researchers to identify, for example, the associ
ation between data aggregation (e.g., firmversus industrylevel data) and
estimated unionproductivity effects while also controlling for other study
characteristics, such as the time span of data. Metaregression analysis
offers a richer framework through which an existing body of empirical
literature can be reviewed.
The basic metaregression equation takes the following form:
The principal use of metaregression analysis
quantitative
assessment of the
Y
i
=
α
+
β
1
N
i
+
γ
1
X
i
1
+
…
+
γ
k
X
ik
+
δ
1
K
i
1
+
…
+
δ
n
K
in
+
ui
(1)
where Y = the partial correlation (or elasticity) derived from the ith study
α = a constant
Ni= sample size associated with the ith study
X = dummy variables representing characteristics associated with
the ith study
K = mean values of quantifiable variables, such as union density
ui = the disturbance term, with usual Gaussian error properties (see
Stanley and Jarell 1998)
Page 10
What Do Unions Do to Productivity? A MetaAnalysis
/ 659
MetaAnalysis Results
The 73 studies are presented in alphabetical order in Table 1 together
with the country to which the data relate, the sample size N used in each
study, the tstatistic (or the average tstatistic in cases where more than one
estimate is used per study), the partial correlation coefficient r, and the
associated unionproductivity effect, as well as the publication outlet. The
unionproductivity effects are presented in three separate ways. In column
6 we list δ, the establishment effect or the elasticity of productivity with
respect to union density. This is the preferred elasticity measure because it
measures the percentage change in labor productivity for an increase in
union density of 1 percent. As can be seen from Table 1, this elasticity is
not available from most of the studies. For most of the studies that did
report δ, we also present the mean unionproductivity effect—that is, we
evaluate the impact of unions on productivity at sample means. Finally, we
also present the total productivity differential. This is available for most of
the studies and hence will be the central focus of the metaregression ana
lysis. This effect measures the impact of unions in the case of 100 percent
unionization. Studies are divided into those using density and those using
a dummy variable to denote union presence. The regression coefficients on
these are not comparable because the density studies measure δ, whereas
the dummy studies measure the impact on productivity arising from 100 per
cent unionization. While it is true that 100 percent unionization is rare,
by evaluating the total productivity effect, we can compare most of the
studies.5 It can be seen from Table 1 that there is a wide range of results,
with both positive and negative findings. There is no apparent consistency
in the results. The partial correlations from 29 of the 73 studies are not
statistically significant. In 45 of the 73 studies a positive relationship was
found between unions and productivity, and the remaining 28 found a neg
ative relationship. Of these, 26 found a positive and statistically significant
effect, whereas 18 found a negative and statistically significant effect. The
highest positive partial correlation is +0.47, whereas the largest negative
partial correlation is −0.58. The weighted average (using sample size as
weights) of only the negative partial correlations is −0.06, whereas the
weighted average of only the positive partial correlations is +0.07. There is,
however, no reason to separate the studies like this.
5 For studies using the BrownMedoff methodology, with unionization measured as a percentage, the
total productivity effect is the coefficient on unionism after it is converted into a percentage. In the case
where a union dummy variable is used, the total union productivity effect is calculated as the antilog of
the dummy coefficient with 1 subtracted from it.
Page 11
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TABLE 1
E S E A B U P (n = 73)
AuthorCountry
N
Average
tstatistic Average r
δ
Mean
Union
Effect
Total
Union
Effect Outlet
Allen (1984)
Allen (1985)
Allen (1986a)
Allen (1986b)
Allen (1988a)
Allen (1988b)
Argys & Rees (1995)
Arthur (1994)
Baldwin (1992)
Bartel (1994)
Batt (1999)
Bemmels (1987)
Black & Lynch (2001)
Boal (1990)
Bronars et al. (1994)
Brown & Medoff (1978)
Brunello (1992)
Byrne et al. (1996)
Byrnes et al. (1988)
Cavalluzzo & Baldwin (1993)
Caves (1980)
Caves &Barton (1990)
Chezum & Garen (1998)
Clark (1980a)
Clark (1980b)
Clark (1984)
Conte & Svejnar (1988, 1990)
Conyon & Freeman (2002)
USA
USA
USA
USA
USA
USA
USA
USA
Canada
USA
USA
USA
USA
USA
USA
USA
Japan
USA
USA
USA
UK/USA
USA
USA
USA
USA
USA
USA
UK
81
+2.12
+2.31
+1.38
+1.61
+2.79
+2.69
+1.60
+2.24
−1.70
+1.95
−0.52
−1.19
−1.91
+0.54
+1.04
+1.95
−3.23
−0.86
+2.48
+2.13
−1.77
−3.32
−1.75
+2.00
+0.05
−2.33
+2.04
−0.83
+0.244**
+0.237**
+0.253#
+0.190*
+0.223***
+0.470**
+0.029#
+0.416**
−0.141**
+0.160**
−0.038#
−0.108#
−0.078**
+0.035#
+0.039#
+0.139**
−0.103***
−0.075#
+0.241***
+0.239**
−0.270*
−0.202***
−0.019*
+0.195**
+0.025#
−0.034**
+0.170**
−0.068#
+0.15
+0.12
—
—
+0.20
—
—
+0.15
−0.001
+0.42
—
−0.70
—
—
+0.03
+0.16
—
—
—
—
na
−0.005
—
—
—
−0.03
+0.37
—
+5%
+4%
—
—
+6%
—
—
+7%
na
+7%
—
−18%
—
—
—
+5%
—
—
—
—
na
na
—
—
—
−1%
+25%
—
+15%
+12%
+27%
+35%
+20%
+51%
+1%
+15%
−0.1%
+42%
−3%
−70%
−12%
+3%
+3%
+16%
−15%
−11%
+69%
+38%
na
−0.5%
−3%
+10%
−1%
−3%
+37%
−4%
QJE
RES
JLR
ILRR
ILRR
IR
RLE
AMJ
Book
IR
ILRR
ILRR
RES
IRRR
IR
JPE
ILRR
IR
MS
Book
Book
Book
AE
ILRR
QJE
AER
IJIO
Book
102
44
151
306
42
3169
30
167
155
202
46
627
249
670
204
979
128
197
83
47
268
8152
104
465
4681
155
932
Page 12
What Do Unions Do to Productivity? A MetaAnalysis
/ 661
Cooke (1994)
Coutrot (1996)
Craig & Pencavel (1995)
Davies & Caves (1987)
Dickerson et al. (1997)
Eberts (1984)
Edwards & FieldHendrey (1996)
Ehrenberg et al. (1983)
Fitzroy & Kraft (1987a)
Freeman (1988)
Graddy & Hall (1985)
Grimes & Register (1991)
Guthrie (2001)
Hirsch (1991)
Holzer (1990)
Huselid (1995)
Ichniowski et al. (1997)
Katz et al. (1987)
Kaufman & Kaufman (1987)
Kleiner & Petree (1988)
Kleiner & Ay (1996)
Kleiner & Lee (1997)
Koch & McGrath (1996)
Kurth (1987)
Lee & Rhee (1996)
Lovell et al. (1988)
Machin (1991)
USA
France
USA
UK/USA
UK
USA
USA
USA
Germany
USA
USA
USA
New Zealand
USA
USA
USA
USA
USA
USA
USA
Sweden
Korea
USA
USA
Korea
USA
UK
841
4289
170
+2.59
+2.77
+1.91
−2.05
+0.88
+0.56
+1.36
+0.36
+2.85
+0.86
−1.47
+2.26
+1.00
−6.10
+1.10
+1.00
+1.50
+1.95
−0.64
+2.64
−0.85
−0.13
+0.68
−3.19
−2.33
−2.49
−0.89
+0.090***
+0.048***
+0.152***
−0.236**
+0.086#
+0.010#
+0.146#
+0.024#
+0.260***
+0.034#
−0.193#
+0.050**
+0.090#
−0.077***
+0.196#
+0.180#
+0.032#
+0.380*
−0.114#
+0.120***
−0.183#
−0.010#
+0.039#
−0.464***
−0.196**
−0.486**
−0.063#
—
—
—
—
—
—
—
+29%
+7%
+29%
−13%
+2%
+6%
+18%
9%
+9%
+12%
−11%
3%
13%
−8%
+0.03
+0.1%
+1%
na
−10%
+60%
−40%
−1%
+34%
−8%
−1%
−68%
−13%
ILRR
TE
BP
Book
IRAE
ILRR
RLE
ILRR
QJE
EER
JLR
IR
AMJ
Book
IR
AMJ
AER
BP
JLR
Book
AILR
IR
SMJ
JLR
JLR
JLR
ECO
86
98
−0.133
+0.02
—
—
—
+0.09
+0.12
—
—
+0.13
−0.08
—
+0.001
—
—
—
+0.6
−0.40
—
+0.34
−0.08
−0.01
−0.68
—
+0.2%
—
—
—
+3%
+4%
—
—
+4%
−3%
—
+0.01%
—
—
—
+7%
−34%
—
+7%
−1%
−0.2%
−16%
—
3251
96
256
123
650
60
2062
136
6248
1320
855
2190
33
37
490
29
184
318
50
144
26
208
AuthorCountry
N
Average
tstatisticAverage r
δ
Mean
Union
Effect
Total
Union
EffectOutlet
Page 13
662 /
C D P L
Maki (1983)
Meador & Walters (1994)
Mefford (1986)
Milkman (1997)
Mitchell et al. (1990)
Mitchell & Stone (1992)
Morishima (1990)
Muramatsu (1984)
Noam (1983)
Pencavel (1977)
Register (1988)
Register & Grimes (1991)
Schuster (1983)
Tachibanaki & Noda (2000)
Torii (1992); Torii & Caves (1992)
Warren (1985)
Wilson (1995)
Wilson & Cable (1991)
Canada
USA
USA
USA
USA
USA
Japan
Japan
USA
UK
USA
USA
USA
Japan
Japan
USA
USA
UK
183
889
126
2684
886
+2.47
−1.99
+4.19
+1.38
+1.70
−3.00
+1.00
+1.49
+0.33
−4.09
+3.86
+2.01
+2.35
−3.02
−0.135
−3.12
+0.96
−2.28
+0.182**
−0.067**
+0.360***
+0.029#
+0.084*
−0.331***
+0.131#
+0.094#
+0.010#
−0.501***
+0.250***
+0.058**
+0.259***
−0.091***
−0.013#
−0.583***
+0.112#
−0.146**
+0.33
—
—
—
—
—
+0.001
+0.12
+0.01
−0.22
+0.17
—
—
—
0
−0.81
+0.14
—
na
—
—
—
—
—
33%
−13%
+33%
+19%
+5%
−13%
+0.1%
+12%
+3%
−22%
+19%
5%
17%
−50%
0
−81%
+14%
−18%
RI
JLR
ILRR
JLR
Book
ILRR
IR
Book
RLE
BJIR
JLR
JLR
ILRR
Book
Book
JLR
Book
AE
83
69
+0.05%
+2%
—
−3%
—
—
—
—
0
−19%
+5%
—
515
1100
56
389
1229
474
2358
124
26
266
260
*, **, *** correlation is statistically significant at the 10, 5, and 1 percent levels, respectively. # Not statistically significant. na denotes that the productivity effect cannot be derived
from the study. Journal codes are as follows: AE: Applied Economics; AER: American Economic Review; AMJ: Academy of Management Journal; BJIR: British Journal of
Industrial Relations; BP: Brookings Papers; ECO: Economica; EER: European Economic Review; IJIO: International Journal of Industrial Organization; ILRR: Industrial & Labor
Relations Review; IR: Industrial Relations; JLR: Journal of Labor Research; JPE: Journal of Political Economy; MS: Management Science; RI: Relations industrielles; RES:
Review of Economics and Statistics; RLE: Research in Labor Economics; QJE: Quarterly Journal of Economics; TE: Travail et Emploi.
AuthorCountry
N
Average
tstatisticAverage r
δ
Mean
Union
Effect
Total
Union
EffectOutlet
TABLE 1 (cont.)
Page 14
What Do Unions Do to Productivity? A MetaAnalysis
/ 663
In order to conserve space, only the key and more interesting metaanalysis
results are presented and discussed. The traditional metaanalysis results
are presented in Table 2 for five different groupings of studies. The table
presents information on the number of studies included in each meta
analysis, the combined sample size of the included studies, and the unweighted
mean, median, and sample size weighted mean partial correlations. The
range shows the spread of actual results reported in the literature. The
95 percent confidence intervals are presented in brackets, and these incorporate
the variance associated with the estimated average partial correlations.
These can be used to test the hypothesis that the unionproductivity effect
is zero, positive, or negative. An important consideration is whether the
partial correlations are drawn from a group of studies that is homogeneous.
A chisquare test for this is presented in Table 2, testing the hypothesis that
all the partial correlations are equal. If this hypothesis is rejected, then it is
important to search for factors that lead to heterogeneity.6
There is also the issue of possible differences in the quality between stud
ies. Our starting position was to rank all the studies equally. The issue of
quality may be reflected in the publication outlet. Of the 73 studies listed in
Table 1, 13 were published in the Industrial and Labor Relations Review, 11
in the Journal of Labor Research, 9 in Industrial Relations, and 13 in leading
economics journals (such as the Journal of Political Economy, American
Economic Review, and the Quarterly Journal of Economics).7 We infer from
this that the published empirical literature is of a very high quality and that
there is no basis for distinguishing articles on the basis of publication out
let. That is, it is not valid to argue that there is a significant portion of the
studies that were not good enough to get into “good journals.”
As is common in metaanalysis, we use the sample size to construct
weighted means. Thus a study with a larger sample size is given greater
weight regardless of employment levels. There is no way of getting around
this problem because few studies report employment levels. It is not possible
to use employment levels as weights. This problem affects the metaanalysis
but not the metaregression analysis.8 In addition to using sample sizes as
weights, we also used two alternative weighing methods. The first involved
using citations as weights. Citations were derived from the Social Science
Citations Index. In effect, this is equivalent to using what the profession
6 A technical appendix is available from the authors detailing the formulas (weighted mean, confi
dence intervals, and the heterogeneity test) used in the metaanalysis. All the metaanalysis calculations
were made using MetaWin version 2. (Rosenberg et al. 2000)
7 This does not imply that the studies published in the other journals are of inferior quality.
8 For comparison purposes, we report both the unweighted and the weighted measures of central
tendency.
Page 15
664 /
C D P L
TABLE 2
MA U P, P C P E
All Studies (2)U.S. Studies (3)U.K. Studies (4) Japanese Studies (5)
U.S.
Manufacturing (6)
Number of studies
Total sample size
Mean r
73 55 75 10
58 403
+0.03
47 549
+0.05
1 687
−0.17
4 045
−0.01
5 004
+0.12
(−0.21 to +0.26)
+0.03
+0.01
(0.00 to +0.02)
+0.04
(+0.01 to +0.06)
−0.58 to +0.47
511***
(−0.23 to +0.32)
+0.04
+0.02
(+0.01 to +0.03)
+0.06
(+0.03 to +0.09)
−0.58 to +0.47
395***
(−1.00 to +0.75)
−0.15
−0.10
(−0.16 to −0.04)
−0.15
(−0.28 to −0.01)
−0.46 to +0.09
19**
(−1.00 to +1.00)
−0.01
−0.08
(−0.13 to −0.04)
−0.03
(−0.18 to +0.11)
−0.18 to +0.13
28***
(−0.59 to +0.84)
+0.11
+0.07
(+0.04 to +0.10)
+0.10
(+0.01 to +0.20)
−0.20 to +0.42
62***
Median r
Weighted mean r
Random effects weighted mean
Range
Heterogeneity
Productivity Effects
−0.09 [−0.18]Unionization elasiticity
+0.01 [+0.07]
+0.01 [+0.08]na
+0.08 [+0.01]
Total productivity effect
Unweighted
Sample size weighted
Ranking size weighted
+4%
+1%
+7%
+7%
+3%
+7%
−11%
−8%
−14%
−13%
−32%
−13%
+10%
+10%
+2%
N: Figures in parentheses are 95 percent confidence intervals. **, *** Coefficient is statistically significant at the 5 and 1 percent levels, respectively, Chisquare test. na means
sample size too small to calculate average elasticity.
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