Forthcoming in LABOUR 2011.

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ADOPTION AND TERMINATION

OF EMPLOYEE INVOLVEMENT PROGRAMS



Running title

Adoption and Termination of EI Programs



Wei Chi*

Associate Professor

Tsinghua University



Richard B. Freeman

Professor

Harvard University and NBER



Morri
s M. Kleiner


Professor

University of Minnesota and NBER

Forthcoming in LABOUR 2011.




Abstract


Using an interview survey of manufacturing establishments that provide 10
-
years of
retrospective data on labor practices, we investigate factors associ
ated with the adoption
and termination of employee involvement programs and the relation between these and
other human resource policies. In the period studied, more firms introduced than
terminated such programs but a sufficiently large number chose to e
liminate such
programs to indicate that employee involvement does not fit in all business settings. Our
results show that business strategy and the use of other complementary human resource
policies affect the dynamics of EI use in U.S. manufacturing esta
blishments.


*Corresponding author: e
-
51 School of Economics and Management, Tsinghua
University, Beijing, China, 100084. Tel.: (8610) 62795072. Fax: (8610) 62772021.
Email: chiw@sem.tsinghua.edu.cn.

1


Adoption and Termination of Employee Involvement Progr
ams


1. Introduction



Employee involvement (EI) programs

the diverse set of personnel and human

resource (HR) practices that increase workers’ authority at workplaces and in business
decision making, such as total quality management, self
-
directed work
teams, and
workplace committees

are widely heralded innovations in the US. Impressed by these
programs, the federal government’s Commission on the Future of Worker
-
Management
Relations recommended in its 1994 report that the government encourage EI progra
ms to
improve the quality of work life and productivity (Commission on the Future of Worker
-
Management Relations, 1993

1994).

Most analyses of EI focus on the factors that lead firms to adopt EI policies and
on the impact of EI on outcomes. Studies show t
hat adoption of EI is more likely in
competitive product markets among firms that respond to the market quickly and
flexibly; use new technologies that require skilled workers; have business strategies that
emphasize quality and innovation rather than low
cost; and that adopt complementary HR
practices, such as high levels of training and incentive compensation plans

(Arthur, 1992;
Osterman, 1994, 2000; Ichniowski and Shaw, 1995; Pil and MacDuffie, 1996; Dunlop
and Weil, 1996; Gittleman, Horrigan, and Joyce
, 1998, and Lynch, 2007).


Other studies
also show that EI promotes higher productivity, improves quality, raises customer
satisfaction and sales, and reduces quits and improves worker satisfaction (Arthur, 1994;
MacDuffie, 1995; Huselid, 1995; Banker et
al., 1996; Berg et al., 1996; Dunlop and Weil
1996; ; Ichniowski, Shaw, and
Prennushi
, 1997; Freeman and Rogers, 1999; Freeman and
Kleiner, 2000; Batt, 2002; Batt, Colvin, and Keefe, 2002; Hunter, MacDuffie, and
Doucet, 2002; Bartel, 2004; Black and Lynch,

2004a, 2005). However, Godard’s (2004)


2

review indicates that EI is not a universal solution to the problems faced by a firm. Some
studies report that EI has modest impacts on outcomes that may not justify the cost of
these programs (Freeman and Kleiner,

2000; Cappelli and Neumark, 2001)
.
Other
studies emphasize that the effects differ because business strategy and other business
characteristics and market factors are intermediaries in the relationship between EI and
financial performance

(Huselid, 1995;

Youndt et al., 1996)

Consistent with this analysis, there is a moderate rate of termination of EI
programs, particularly for quality of work life and quality circles (Goodman, 1980;
Rankin, 1986; Drago, 1988; Eaton, 1994).

E
aton (1994) estimates a 20 pe
rcent failure
rate for employee involvement practices in union establishments. Kleiner, Leonard, and
Pilarski (2002) present evidence of firms abandoning poorly performing EI programs.
Black and Lynch (2004a, 2004b)

document that a substantial number of f
irms dropped or
lowered the percentage of employees participating in job rotation, profit sharing, or self
-
managed teams between
1993 and 1996. Despite the failure of some EI programs, there
is little quantitative analysis of the reasons for the terminati
on of EI, and no analysis of
decisions to introduce EI and to terminate EI in the same framework.

This study uses detailed on
-
site interviews of managers and workers in 51
manufacturing establishments on the history of their EI and related policies over
ten years
to treat the decisions together in the same analytic framework. Retrospective reports on
10 years of EI programs allows us to create a pseudo
-
longitudinal data set to examine the
years in which firms adopted or terminated EI programs and their re
lation to other human
resource and management practices. Our sample size is small for a quantitative analysis,
but it is large for detailed on
-
site interview analysis that usually involves no more than a
handful of companies. We employ a Markov chain mode
l to examine the movement of


3

firms to a long
-
term equilibrium of EI use in a way that can be applied to other studies of
the timing of adoption and termination of human resource programs.

2. Survey Design and Implementation



To describe and analyze the

dynamics of EI use, we gathered data on the labor
relations practices of manufacturing establishments from 1986 to 1995, which covers the
period when EI programs increased rapidly. We identified establishments from the
Census of Manufacturers, which the
U.S. Census Bureau has conducted every 5 years
since 1967.
1

We obtained the name and address of establishments surveyed by the
Census. We randomly selected establishments that had at least 50 employees
at the time
they were drawn from the Census of Manufa
cturers in 1993

and were located in the Mid
-
West to minimize travel costs. Some plants provided us with contact information of their
affiliate plants that were outside the Mid
-
Western region of the U.S. To increase sample
size, we contacted these affiliat
e plants as well, which provides us data on some
establishments within the same firm that we use in the analysis. In total, we contacted
111 plants, of whom fifty
-
one were willing to participate and allow researchers to visit
the plant, collect administrat
ive data, and interview management and labor. The response
rate of 46 percent is higher than most mail or telephone surveys.

We collected the data on the history of the adoption and termination of employee
involvement and other labor and business pract
ices through on
-
site visits in 1995
-
96 on
the notion that on
-
site interviews would provide more accurate data than

phone or mail
surveys. The survey team interviewed general and human resource managers, workers,
and union representatives at the plants. A
ll the data was collected in one visit except in
five cases where the manager/administrators did not have information and requested that
we return. We examined company records to obtain or verify some factual data such as


4

turnover, number of employees, et
c. The surveyor(s) asked the company representatives
the questions and waited for the representatives to reach a consensus for each question
before filling out the survey response.

The survey team also collected documents about
the business environment, t
echnology, and production of the plants during the visits.


The survey team asked whether the plant had adopted particular employee
involvement programs and other human resource programs between 1986 and 1995, and
if so, in which year. We then asked whet
her the program was still in use, and if not, the
year the establishment terminated the program. The employee involvement practices
covered were job rotation, suggestion systems, quality of work life, quality circles, total
quality management, self
-
manage
d work teams, job redesign, joint labor
-
management
committees, and employee representation on the board of directors. These policies focus
on the workplace governance of the organization. In addition, the survey asked about the
adoption and timing of
sel
ection and staffing

policies

whether the company had a
detailed screening process, personal interview, aptitude test, physical exam, reference
check, and probationary period;
training

whether the company offered on
-
the
-
job
training, training in team buildi
ng, on
-
site training, and tuition reimbursement;
performance appraisal

policies

whether the company used assessment centers, formal
review sessions, and a standardized form to evaluate their employees periodically. The
survey also asked about
financial in
centive programs

whether the company had an
individual incentive plan, employee stock ownership, cash or deferred profit sharing, gain
sharing, skill
-
based pay, employee stock purchase plan, and group bonuses


that should
motivate workers on employee inv
olvement committees.

The survey team also asked about business strategies and management policies. It
identified four categories of business strategy: growing market share, obtaining a market


5

niche, maximizing shareholder value of the firm, and short
-
ter
m profit maximization. We
chose these categories to reflect whether the firm had a short
-
term or long
-
term
perspectives on the notion that plants with a long
-
term orientation, such as those seeking
a larger market share or increasing shareholder value, wou
ld be more likely to use the EI
practice than the plants with a short
-
term profit focus.
2

We asked the senior managers to
rate the establishment’s emphasis on these strategies on a 1 to 5 scale with 1 indicating “a
little” and 5 being “a great deal.” The s
enior manager and other managers also told us
whether the plant had undergone major restructuring since 1986 and, if yes, the year, and
number of times it had restructured; and whether the firm had changed the plant
manager/production leader since 1986.

The basic information on the plants included the year the plant was built, the
number of employees in the plant, whether the company had union representation, and
the average yearly turnover rate. The establishments were generally built in the 1950s
and 1
960s, with an average age of 37 years. Three plants in the sample were, however,
built later than 1986 and thus could not provide data on practices and policies in some
years. This reduced the total number of plant
-
year observations from 510 to 496. The
plants had relatively large numbers of employees, with an average number of employees
of around 1100; only 5 plants had fewer than 100 employees. Twenty
-
seven of the 51
establishments had its production employees unionized. Eighteen plants are from sev
en
multi
-
establishment companies, while the others are single
-
establishment. The
establishments in the sample produce a wide range of products from foods and shoes, to
automobiles, electrical products, and steel, with the majority (65%) in durable goods.

The Appendix Table gives precise definition and descriptive statistics of the variables.

3. Qualitative information



6

During the interviews, we obtained considerable information about EI programs
that goes beyond the data from the structured survey. Ma
nagers cited reasons like
“improving morale, changing technology, or just following the trend” for adopting EI.
During the 1990s employee involvement was one of the major “fads” in U.S. industry.
One establishment in auto parts stated they had hired a con
sultant to review their
production processes, and he recommended an employee involvement program, which
they implemented. Others reported that new MBAs who had learned about EI in Human
Resources classes had recommended these programs to management. Some
managers
also said their plant “followed the leader.” An aerospace company had heard about the
success of an EI program in auto manufacturing and hired the HR Vice President away
from the auto firm to implement EI in the aerospace company. Several managers

said
they had heard about firms’ greatly increasing productivity through employee
involvement, and decided to adopt them.

Our interviewees also told us about some of the challenges they encountered after
introducing the EI, which in some cases led to the

plant terminating EI policies. From the
interviews these reasons seemed idiosyncratic to the establishment. In one unionized auto
plant the union president wanted to negotiate the termination of EI because he thought it
resulted in increased productivity
with fewer employees but offered “no job protection
from management.” A senior manager in aerospace observed that EI meetings “take too
much time away from the production line.” Another auto parts manufacturing manager
said it was “too costly and didn’t de
liver on quality.” Overall, the interview process
suggests that many of the firms adopted an EI program in a trial
-
and
-
error process,
following the prevailing practice and then terminated the program if they encountered
unexpected problems.



7

4.
Trends in
EI Use

The definition of exactly what programs define an EI system or create an
associated high
-
performance workplace is a fuzzy one. Some analysts include in their
definition alternative work practices and job design that promote workers’ participation
on

the job, such as job rotation, teams, TQM, quality circles (Osterman, 1994, 2000).
Others treat a broader set of HRM practices including selection, training, appraisal, and
incentive compensation (Godard, 2004). We adopt the narrower definition of EI, an
d treat
incentive pay, selection, training, and appraisal programs as other human resource
management practices that may complement the EI policy. Specifically, we focus on the
nine programs listed in Table 1, which are directly related to workplace govern
ance
issues
3

and use a terminology familiar to interviewed managers and workers. Since the
specific programs listed are often used together and since there are terminological
differences among respondents, there is invariably overlap among some of the pro
grams
which could be viewed as part of the same innovation. For example, if a company
implements self
-
managed teams, it must involve some degree of job redesign; the
company might report both innovations or one or the other, depending on how it
introduced

the changes.

Table 1 reports the mean proportion and standard deviation of the nine EI
programs among the establishments yearly from 1986 to 1995. The table shows an
upward trend in EI use in our sample. In 1995 job rotation and joint committees were th
e
most common programs. Seventy
-
five
percent of the plants in the sample had job rotation
while 67 percent had joint labor management committees. Forty
-
seven
percent of the
establishments in the sample had adopted TQM by 1995

just slightly higher than the
42
percent reported by Black and Lynch (2004a) based on national survey data for 1994.



8

Worker participation on corporate boards is the least used policy. Note that the
prevalence of some EI programs fell over the period. The percentage of establishments
using quality circles dropped by 6 percentage points from 1986 to 1995. In addition, the
table shows that the upward trend in EI use varies over time: there are periods when
many establishments adopt programs, and periods when more are terminated.

Table

2 shows the correlations in 1986
-
1995 among our EI policies and between
them and three HR policies that are often associated with EI: selection, performance
appraisal, and training. The EI programs are generally positively correlated with each
other: the
average of the correlation coefficients among them is 0.21, which suggests that
while the programs are used together, plants pick and choose among them (or possibly
use somewhat different terminologies). The EI programs are also moderately positively
corr
elated with the training, selection, and performance appraisal: an average correlation
coefficient of 0.13. Figure 1 aggregates the data on individual EI programs to display the
number programs in use in 1995 and gives the number of other HR programs (whi
ch goes
beyond the three examined in Table 2). This shows a substantial positive relation between
other HR programs and EI programs with a correlation of
0.29.

It is reasonable to expect that plants belonging to the same company adopt similar
practices du
e to corporate level decisions or the greater ease of learning what works
within a firm than learning what works in plants belonging to other firms. This suggests
that EI practices are likely to be more similar within companies than across plants. A
natur
al measure of similarity for vectors of 0/1 variables, such as our list of EI practices,
is the sum of the absolute difference between the presence or absence of each individual
program between two plants (which is related to the Hamming (1950) distance me
asure
of information theory). For example, if plant 1 of a multi
-
plant company had job rotation,


9

and plant 2 of the same company did not, the difference in job rotation would be one.
Add up the differences across 9 EI programs for each year gives a measur
e of the
difference in EI practices that varies between 0 and 9. The larger the number, the greater
the differences between the plants. In the case when a multi
-
plant company has more
than 2 plants, we calculate the number of differences in EI between all

pairs of plants and
take the average across all the pairs as our measure of the distance. For instance, in a
company with 3 plants we would have average the 3 pairs; in a company with 4 plants,
we would average the 6 pairs. For comparison with single
-
p
lant companies we calculate
the average total number of differences across the 33 single
-
plant companies.

Table 3 gives the results of these calculations. Using a t test, two of the seven
multi
-
plant companies have significantly smaller within
-
company di
fferences in the
presence/absence of EI than do single
-
firm plants but two of the multi
-
plant companies
have a significantly larger within
-
company differences. The remaining three show no
significant differences compared in our measure than the average o
f the 33 single
-
company plants. Thus, these data do not support the view that the variation of EI among
plants within a company is smaller than the variation of EI across plants in different
companies. The implication is that introduction/termination of E
I in our sample is largely
a plant level decision rather than the result of corporate policy.

Table 4 shows the number of plants with specified EI programs in the initial year
1986 and the number of plants adopting or terminating programs each year to 199
5. The
numbers associated with individual programs in particular years show the number of
firms that introduce programs or the number that terminate them (those with negative
signs). For instance, the 3,
-
1 statistics for joint committees in 1991 tell us
that 3
establishments adopted a joint committee and one establishment terminated its joint


10

committee program. The figures giving total numbers of adoptions and terminations
show that in most years some firms adopt programs while others terminated them.
Co
nsistent with the national trend in EI over the period, the number of EI policies in
place increased from 134 in 1986 to 204 in 1995

a 52 percent change. This change
resulted from 112 additions of programs and 42 terminations. We also measure the
dynamic
s of EI use in terms of the average number of years of EI use through 1995.
These statistics, given in Table 5, show that plants that terminated an EI program had the
program for at least 4 years. Those who kept the program had them for approximately 9
ye
ars (which is the average duration across the programs) through 1995. Of our nine EI
programs, self
-
managed work team and job redesign were adopted most recently, and
thus have the shortest average durations in use in 1995.

Figure 2 shows the percentage o
f plants using zero, one, or multiple EI policies in
1986 and 1995 and the percentage of plants by number of EI policies that would have
resulted in 1995 if firms chose their EI plans by random draws from an urn with a
probability consistent with the obser
ved number of programs in 1995. Specifically, in
1995 the 51 firms had established 204 EI programs out of a possible 459 programs (= 51
firms x 9 EI programs), giving the probability of a single program of 44.4%.
4

Between
1986 and 1995 the percentage of p
lants with zero policies declined significantly. Given
the relatively small sample size, the distributions are lumpy with concentrations of plants
at 0, 1, and 3 in 1986 and with what an exceptionally large number of plants at 8 in 1995.
The comparison
of the actual distribution of firms by numbers of programs in 1995 with
the random draw distribution shows a greater concentration of establishments at the tails
of the distribution than expected under the random model. For instance, in the actual
distrib
ution 12% of firms have more than 7 EI programs while the random model predicts


11

that just 5% would have more than 7 programs. Similarly, in the actual distribution 26%
of firms have 2 or fewer EI programs while the random model predicts 16%. This pattern
suggests that the EI policies are complementary in the sense that firms either have a lot or
a few relative to what they would be if the policies were independent.
5


Finally, while our analysis is limited to the 51 surveyed establishments, the
pattern of g
rowth of EI over time fits with results from nationally representative samples
(Lynch 2007) to suggest that our findings on the dynamics of the EI introduction and
termination may generalize outside of the sampled establishments.

5. Measuring Intensity of

EI

Many studies measure the intensity of EI use by forming a summated rating of the
intensity of specific EI programs using the particular scaling in the survey (Ichniowski
and Shaw, 1995; Pil and MacDuffie, 1996). This treats each program the same. As
an
alternative procedure, we used the latent variable Rasch model from educational testing
to form an index of employee involvement. In educational tests the Rasch model gives a
bigger score to a correct answer on a problem where few students give the cor
rect answer
than to a correct answer on a problem that many students get right. Analogously, our
Rasch index gives greater weight to policies that are relatively rare in forming an index of
the establishment’s intensity of EI. The virtue of the Rasch mod
el is that it weighs more
heavily programs that are rare and thus potentially more indicative of the intensity of the
firm's commitment to EI. Formally, we posit the probability that a plant with a certain
program is a function of plant and EI policy char
acteristics:

(1)
( 1) (,)
ij i j
PX


,
i
, establishment,
j
, EI practice,

where

denotes the index of employee involvement in an establishment, and


indicates
the rarity of th
e particular EI program. The probability that an establishment had a


12

specific policy depends on an establishment’s degree of EI use (

) and the frequency
level of an EI program (

). The function


is specified to have a log
istic form, giving
equation (2):


(2)
exp( )
( 1)
1exp( )
i j
ij
i j
PX




 
,
i
, establishment,
j
, EI practice.



We use maximum likelihood to estimate the establishment parameter (

) and the
EI policy p
arameter (

).
6

The estimate of


is our measure of EI use in plants. Its value
ranges from

1 to 1. While conceptually preferable to a simple count of the number of EI
programs that an establishment has, the Rasch measure is highly co
rrelated with a
summated rating count variable (
r

= 0.98).



To examine

the dynamics of adopting or terminating EI programs in a given year,
we use a 0/1 dependent variable that measures whether or not the firm added at least one
EI program between year
t

and year
t
+1; and whether or not the firm dropped at least one
EI program between year
t

and year
t
+1. To assess how explanatory factors affected these
decisions, we estimated the following models:

(3)

1
it it i t it
AX


 
,

1
it it i t it
TX


 
,

where
A
it

is the measure of EI adoption in year
t
, and
T
it

measures EI termination in year
t;

X
it
-
1

are a set of time
-
varying explanatory variables lagged one year; and where
i


are
individual plant dummy variables, which control for plant fixed effects, and
t


are year
dummy variables. The two equations are estimated using the Least Square Dummy
Variable (LSDV) method, and specified as the linea
r probability model with White robust
standard errors. The LSDV estimators show how much a change in an explanatory
variable affects the probability.
7




13

Columns 1
-
4 of Table 6 reports the estimated coefficients and standard errors on
variables in regression
s of the level of EI use on the explanatory variables for pooled
observations for all establishments and years. The odd
-
numbered regressions include
variables measuring plant
-
specific time
-
invariant characteristics, such as union status or
durable manufac
turing. Since there may be other plant
-
specific variables impacting the
adoption and termination decisions, the even
-
numbered regressions drop these time
-
invariant variables but include year and plant dummy variables to control for all observed
and unobser
ved plant fixed effects
.

The odd
-
numbered regressions are embedded in the
more general even
-
numbered models.

The first two columns use our Rasch measure of EI as the dependent variable. To
see whether the results are sensitive to the EI measure used, c
olumns 3
-
4 estimate the
model using the summated rating index of EI that give the same weight to different EI
programs.

This links our results to those in earlier studies that give each EI policy the
same weight. The estimates in columns 1 and 3 show tha
t c
ompanies with group bonus
and gain
-
sharing and profit
-
sharing have higher EI use than companies without these
programs. This is consistent with the notion that EI programs are complementary with
these programs. In addition, individual incentive pay is
also positively related to the
extent of EI. Unionized firms and larger plants are less likely to have EI programs. The
coefficients on the measures of management strategy show that firms that emphasized the
growth of market share made greater use of EI
than managements that emphasized other
strategies. The estimates in columns 2 and 4, which include plant dummy variables and
less important, year dummies, give weaker results on some of the variables such as profit
sharing but find that firms that have pro
grams to train and appraise workers were more
likely to use EI.



14

Columns 5 through 8 turn to the dynamics of the process. They give estimates of
the determinants of adoption and termination of EI use. We expect variables to have
opposite effects on adoptio
n and termination. Factors that cause firms to adopt EI may
also explain why the firms keep the policies and thus reduce the rate of termination. This
is generally the case in these calculations. The estimated influence of the existing level of

EI in the

top line is significantly negative in the adoption equation and significantly
positive in the termination equation. Comparing
coefficients for other variables that are
significant in at least one of the equations, the coefficients have opposite signs in 5

of 7
cases. For instance, the column 6 estimates show that
establishments that emphasize
market growth are more likely to adopt EI while the column 8 estimates shows that
emphasizing market growth reduces the likelihood of terminating a program. Firms
s
eeking niches in the market are less likely to introduce EI programs in the adoption
equation but more likely to terminate programs in the termination model.


6. Markov Analysis

The evidence in our sample that firms added and terminated EI programs in the

decade under study indicates that they were involved in a dynamic adjustment process
that had not settled down to some equilibrium mix of programs. With nine EI programs
and just ten years of data, a time series regression model will not readily illumina
te the
dynamics of the adjustment process nor predict the possible equilibrium state to which
the firms might be heading. As an alternative, we apply a Markov chain model to our
data to exploit the longitudinal dynamics. The Markov model assumes that a s
ystem is
represented by a set of states, which we specify as the number of EI programs that a firm
has in a given year.
8

The model posits that firms change the number of programs
according to fixed transition probabilities dependent solely on the current s
tate. For


15

instance, if a firm had no EI programs, the model might predict a 20% chance of moving
to one program, a 30% chance of moving to two programs, a 5% chance of moving to
three programs, and a 45% chance of maintaining the zero program status quo.

A firm in
the state of having three EI programs would have different transition probabilities,
allowing for termination of some of those programs or adoption of other programs.

The process is represented by a transition matrix that gives the probability t
hat the
firm changes the number of programs by adopting new ones or terminating existing ones
in the next period, or that it maintains its current EI system. Traditional matrix algebra
analysis of Markov models allows us to predict whether the EI use of
the establishments
converges to a steady
-
state distribution, and if so, the distribution of EI use among the
establishments at the equilibrium.


Table 7 gives the Markov matrix that we use to analyze the adoption and
termination of EI programs. The eleme
nts of the matrix are obtained by averaging all
transitions in the data, regardless of year. The averaging is simple. If five firms had three
programs in year
t

and none changed their number of programs and five (possibly
different) firms had three progra
ms in year
n

and three of the firms increased their
programs to four while two firms reduced them to two, our estimated transition matrix
would have a probability of staying with three programs of 0.5, of increasing to four
programs of 0.3, of decreasing t
o two programs of 0.2, and zero probability of other
transitions. The table groups the eight and nine programs together because we have few
observations for that part of the distribution.
9


The summary statistics in the table give the equilibrium distribut
ion from the
Markov analysis and contrast that distribution with the initial distribution in 1986 and the
distribution in 1995. It shows that the initial distribution moved toward the equilibrium


16

over the period. As a measure of the distance between distr
ibutions, we take the sum of
the absolute value of the difference between the proportions in different states.
10

In 1986,
the actual distribution differed from the equilibrium distribution by 80 percentage points.
By 1995, the sum of the absolute value in

the differences between the distribution and the
equilibrium distribution was just 16 percentage points. The model predicts that the
distribution would reach the equilibrium by 2006. Thus, the adoption and termination
process takes about 20 years to equil
ibrate, with much of the movement toward
equilibrium occurring within 10 years. Examining longitudinal human resource policy
data for the U.S. economy Lisa Lynch finds that manufacturing firms coalesce around a
core set of policies in the mid
-
to late 1990
s (Lynch, 2007).

7. Conclusion


Our analysis of 10
-
year retrospective data from on
-
site visits to establishments
has found that even in a period of increasing EI use, many establishments terminate EI
programs. On
-
site interviews with the plant manager
s, workers and union representatives
suggested that the process of adopting and terminating EI is a trial
-
and
-
error process
which has a lot of uncertainties. But beneath this pattern our data reveal empirical
regularities. In our sample establishments we
re more likely to adopt EI programs and
less likely to terminate them when they had other advanced human resource practices and
when their business strategy emphasized growth of market shares. The factors that lead
firms to terminate programs paralleled
those that lead firms to introduce them, suggesting
that both decisions reflected the aspects of the plants situation. Moreover, the
distribution of numbers of EI practices appears to converge over time. Our Markov chain
analysis suggests that the proces
s reaches a steady state distribution in about 20 years
with most changes occurring during the first 10 years.




17

Notes


1
The

Census of Manufactures asks all establishments in manufacturing (over 200,000
plants) about the
business they conduct, geographic l
ocation, type of ownership, total
revenue, payroll, and employees. The data are confidential and only available with an
application to the Census.

2
The commonly used strategy categories in the literature are low cost, differentiation
(such as innovation o
r quality and service) and focus strategies (Porter, 1980; Delery and
Doty, 1996; Cabrera et al., 2003). The niche market strategy we asked is similar to the
focus strategy.

3

They are consistent with theory and empirical approaches developed by Ichniow
ski
and Shaw (1995), and
Ichniowski

,
Shaw,

and
Prennushi,

1997).

4

An alternative calculation would use a multinomial which takes account of the
difference in the frequency of each particular program.

5

We find a similar pattern if we examine the introd
uction or termination of plans.
Plants introduce 5 or 6 policies at once in 7.5% of the cases when they adopt EI
programs, whereas if they adopted plants independently, they should almost never
introduce 5
-
6 plans at once. Similarly, in 8% of termination
s plants terminate 2 or more
programs in 24% of the times they terminate 2 or more plants. Given the data in Table 4
both patterns are highly unlikely.

6

We use the Quest computer software package to estimate the Rasch measure of EI
system.

7
Estimates us
ing probit and logit specifications showed similar results and are
available from the authors.

8

The alternative would be to take each possible combination of EI practices as a
possible state for the firm, but with nine different programs, there would be 5
12 (= 2
9
)
possible combinations, which is non
-
tractable with our data. Thus, rather than modeling
the links between specific programs, we make the distribution of the number of programs
the target of analysis.

9
In fact, because we have so few observation
s on firms with eight policies, it turns out
be an absorbing state, which is highly unlikely with additional observations.

10
This is a widely used measure of the difference between two distributions. Half of the
sum of the absolute value of the differences

gives the amount of change necessary for the
two distributions to be the same.


















18


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20

Table

1.

Means and standard deviations of the proportion of establishments with EI programs by year, 1986
-
1995


Year (number of
reporting plants)

1986

(n=48)

1987

(n=48)

1988

(n=48)

1989

(n=48)

1990

(n=49)

1991

(n=51)

1992

(n=51)

1993

(n=51)

1994

(n=51)

1995

(n
=51)

All years

(n=496)

Job Rotation



0.47

(0.50)

0.47

(0.50)

0.47

(0.50)

0.48

(0.50)

0.53

(0.50)

0.63

(0.49)

0.69

(0.47)

0.73

(0.45)

0.75

(0.44)

0.75

(0.44)

0.60

(0.49)

Suggestion System



0.43

(0.50)

0.45

(0.50)

0.49

(0.51)

0.50

(0.51)

0.49

(0.50)

0.49

(0.50)

0.51

(0.50)

0.55

(0.50)

0.53

(0.50)

0.59

(0.50)

0.50

(0.50)

Quality of Work

Life


0.31

(0.47)

0.31

(0.47)

0.33

(0.47)

0.28

(0.45)

0.31

(0.47)

0.35

(0.48)

0.35

(0.48)

0.37

(0.49)

0.41

(0.50)

0.41

(0.50)

0.34

(0.48)

Quality Circles



0.35

(0.48)

0
.37

(0.49)

0.37

(0.49)

0.30

(0.46)

0.27

(0.45)

0.29

(0.46)

0.22

(0.42)

0.24

(0.43)

0.27

(0.45)

0.29

(0.46)

0.30

(0.46)

TQM



0.35

(0.48)

0.37

(0.49)

0.37

(0.49)

0.32

(0.47)

0.33

(0.48)

0.45

(0.50)

0.43

(0.50)

0.41

(0.50)

0.43

(0.50)

0.47

(0.50)

0.39

(0.49
)

Self
-
Managed
Work Team


0.10

(0.31)

0.10

(0.31)

0.10

(0.31)

0.10

(0.30)

0.16

(0.37)

0.22

(0.42)

0.29

(0.46)

0.35

(0.48)

0.31

(0.47)

0.33

(0.48)

0.21

(0.41)

Job Redesign



0.22

(0.42)

0.22

(0.42)

0.22

(0.42)

0.22

(0.42)

0.25

(0.44)

0.29

(0.46)

0.31

(0.4
7)

0.35

(0.48)

0.41

(0.50)

0.43

(0.50)

0.30

(0.46)

Joint Labor
-

Management
Committee

0.45

(0.50)

0.45

(0.50)

0.43

(0.50)

0.40

(0.49)

0.45

(0.50)

0.51

(0.50)

0.53

(0.50)

0.57

(0.50)

0.63

(0.49)

0.67

(0.48)

0.51

(0.50)

Employee

Representation
on Board of
Directors

0.06

(0.24)

0.06

(0.24)

0.06

(0.24)

0.06

(0.24)

0.08

(0.27)

0.08

(0.27)

0.06

(0.24)

0.06

(0.24)

0.06

(0.24)

0.06

(0.24)

0.06

(0.24)

Source: Interviews at 51 manufacturing establishments.

Notes: standard deviations are in parentheses.



21

Table 2.
C
orrelation coefficients for EI policies and those policies with selected HR practices, 1986
-
1995



Job
Rotation

Suggestion
System

Quality
of work
Life

Quality
Circles

TQM

Self
-

Managed
Work
Team

Job
Redesign


Joint Labor
-
Management

Committee

Employee
Repr
esentation
on Board of
Directors

Selection

Performance
Appraisal

Suggestion
System

0.24*











Quality of work
Life

0.34*

0.25*










Quality Circles

0.02

0.27*

0.30*









TQM

0.14*

-
0.01

0.41*

0.26*








Self
-
Managed
Work Team

0.18*

0.10*

0.46*

0.17*

0.39*







Job Redesign

0.22*

0.08

0.38*

0.10*

0.42*

0.51*






Joint Labor
-
Management
Committee

0.20*

0.20*

0.33*

0.26*

0.37*

0.22*

0.48*





Employee

Representation

on Board of

Directors

-
0.05

0.19*

0.26*

0.29*

0.09*

0.21*

0.12*

0.19*




Selection

-
0.02

0.06

0.18*

0.11*

0.11*

0.12*

0.14*

0.22*

0.05



Performance
Appraisal

0.10*

-
0.09*

0.20*

0.08

0.21*

0.08

0.29*

0.31*

-
0.13*

0.11*


Training

0.25*

0.05

0.20*

-
0.01

0.13*

0.08

0.09*

0.26*


0.21*

0.36*

0.29*

Source: Interviews at 51 manu
facturing establishments.

Notes: * indicates that the pair
-
wise correlation coefficient between the two variables is significant at the 5% level.



22


Table 3.

Differences in EI use between multi
-
plant and single
-
plant companies using the metric of the number

of practices that differs
between establishments by year



1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

Multi
-
plant companies











1 (2 plants)




2.00

2.00

2.00

2.00

4.00

3.00

2.00

2 (4 plants)

4.33

3.83

3.83

4.33

4.17

4.00

4.33

4.33

4.67

4.
67

3 (2 plants)

2.00

1.00

1.00

1.00

2.00

2.00

1.00

2.00

2.00

2.00

4 (3 plants)

3.33

3.33

3.33

3.33

4.00

4.00

4.00

4.00

4.00

4.00

5 (2 plants)





3.00

4.00

4.00

5.00

3.00

3.00

6 (3 plants)

3.33

3.33

3.33

3.33

3.33

4.00

4.00

4.00

4.00

4.00

7 (2 plants)

6.00

6.00

6.00

6.00

6.00

6.00

5.00

5.00

6.00

6.00

Single
-
plant companies


(33 plants)

3.47

3.48

3.49

3.28

3.58

3.84

3.74

3.84

3.83

3.84

Source: Interviews at 51 manufacturing establishments.

Notes: the numbers indicate the total number of differences (i
n absolute values) in nine EIs between the plants belonging to the same company as
well as among the single
-
plant companies. If the number of pairs for comparison is greater than 2, we calculate the average total number of
differences. We test the differe
nces between multi
-
plant and single
-
plant companies, such as multi
-
plant company 1, 2,…,7 versus single plant
companies. Two of the seven multi
-
plant companies have a significantly smaller within
-
company variance in EI than the variance across the
single
-
p
lant companies; two have a significantly larger variance; the rest three are no different from single
-
plant companies.



23

Table 4.

Number of establishments with specified EI programs and numbers adopting (+) or terminating (
-
) programs by year


Adoption of
Policy

1986

(left
-

censored)

1987

1988

1989

1990

1991

1992

1993

1994

1995

Total
Number at
end

Job Rotation

23



1

3

5,
-
1

4

2,
-
1

2


38

Suggestion System

21

1

2,
-
1

2


-
2

3

2,
-
2

1

3

30

Quality of Work Life

15


1,
-
2


2

2,
-
1

1,
-
1

2

2


21

Quality Circles

17

2,
-
1

-
3

-
2

1,
-
2

3,
-
4


1

2

1

15

TQM

17

1

-
2

-
2

3

6,
-
2

1,
-
1

-
1

2

2

24

Self
-
Managed Work Team

5


-
1

1

3

3,
-
1

5

3,
-
3

1

1

17

Job Redesign

11


-
1

1

2

2,
-
1

2

2

3

1

22

Joint Labor
-
Management

Committee

22

-
1

-
1


3

3,
-
1

2

2

3

2


34

Employee Representa
tion on
Board of Directors

3




1

-
1






3

Total Number of Adoptions
and Terminations (
-
)


4,
-
2

3,
-
11

5,
-
4

18,
-
2

24,
-
14

18,
-
2

14,
-
7

16

10

112,
-
42

Number of EI programs at the
51 establishments

134


136


128


129


145


155


171


178


194


204



So
urce: Interviews at 51 manufacturing establishments.

Notes: The numbers associated with individual programs with minus signs give the number of firms in the specified period tha
t terminated
programs, while the positive numbers give the number that introdu
ced the program.



24

Table 5.

The duration of EI use before termination or by 1995




The total number of
adoptions

(
-

number of termination)


The mean
duration before
termination

(years)

The mean duration by
1995 if not terminated

(years)

Job Rotation


40(
-
2)

4

9.3

Suggestion System

35(
-
5)

5

9.4

Quality of Work Life

25(
-
4)

8.5

9.7

Quality Circles


27(
-
12)

4.2

11.1

TQM

32(
-
8)

6.5

7.3

Self
-
Managed Work Team

22(
-
5)

8.3

4.6

Job Redesign

24(
-
2)


10

5.7

Joint Labor
-
Management
Committee

37(
-
3)


7.7


9.8

Employee Representation on
Board of Directors

4(
-
1)


6


14

Source: Interviews at 51 manufacturing establishments.

Notes: The total number of adoptions is the sum of adoptions over time for each EI. Minus signs give the total number of ter
minations o
ver time.



25


Table 6.
Regression estimates for the analysis of the level of EI and adoption and termination of programs



Rasch

Level of EI

(1)

Rasch
Level of EI

(2)

Summated rating
index of EI

(3)

Summated rating
index of

EI

(4)

Adoption

of EI

(5)

Adoption
of EI

(6)

Termination
of EI

(7)

Termination
of EI

(8)

EI value

t
-
1







-
0.057*

(0.017)

-
0.089***

(0.029)

0.017*

(0.010)

0.076***

(0.024)

Union


-
0.745***

(0.147)


-
1.148***

(0.227)


0.004

(0.047)


-
0.013

(
0.028)


Log Plant Size


-
0.242*

(0.133)


-
0.421*

(0.186)


-
0.041

(0.027)


0.0003

(0.026)


Durable Manufacturing


-
0.048

(0.176)


-
0.295

(0.256)


0.088

(0.054)


0.024

(0.029)


Age of plant


-
0.017

(0.009)

-
0.008

(0.039)

-
0.023

(0.014)

-
0.013

(0.054)

-
0.005*

(0.003)

0.022

(0.018)

-
0.0002

(0.002)

0.006

(0.012)

Age Squared/100


0.009

(0.009)

0.001

(0.030)

0.011

(0.013)

-
0.010

(0.043)

0.005*

(0.003)

-
0.017

(0.015)

0.001

(0.002)

-
0.004

(0.010)

Restructuring


t
-
1


-
0.053

(0.215)

0.156

(0.170)

-
0.745**

(0.
354)

-
1.096**

(0.442)

0.029

(0.072)

0.091

(0.100)

0.002

(0.038)

-
0.099

(0.070)

Selection


t
-
1


0.021

(0.044)

-
0.097*

(0.058)

0.080

(0.068)

0.004

(0.109)

0.022

(0.017)

0.023

(0.038)

0.005

(0.010)

0.023

(0.015)

Appraisal


t
-
1


0.155**

(0.080)

0.252**

(0.1
29)

0.287**

(0.113)

0.633***

(0.162)

0.054**

(0.024)

0.096

(0.068)

-
0.017

(0.016)

-
0.037

(0.030)

Training


t
-
1


-
0.062

(0.115)

0.261***

(0.086)

0.152

(0.159)

0.850***

(0.163)

-
0.002

(0.021)

-
0.003

(0.047)

0.011

(0.018)

-
0.009

(0.031)

Individual incentive

pay

t
-
1


0.301**

(0.
143
)

0.568**

(0.
220
)

0.816***

(0.
215
)

0.430

(0.
334
)

0.013

(0.
041
)

0.033

(0.099)

-
0.034

(0.0
27
)

-
0.194***

(0.07
2
)

Gain sharing or group bonus

t
-
1


0.570***

(0.
170
)

0.511**

(0.
200
)

1.191***

(0.
236
)

0.459

(0.
300
)

-
0.030

(0.
046
)

0.08
4

(0.10
2
)

-
0.022

(0.0
36
)

0.071

(0.
062
)

Profit Sharing

t
-
1



0.142**

(0.
058
)

-
0.112

(0.
104
)

0.177**

(0.
090
)

-
0.128

(0.
150
)

-
0.040

(0.
024
)

-
0.010

(0.
044
)

0.011

(0.0
11
)

0.031

(0.
035
)

Niche market


t
-
1


0.100

(0.090)

-
0.119

(0.
095
)

0.163

(0.
148
)

-
0.037

(0
.
149
)

-
0.029

(0.030)

-
0.070*

(0.04
2
)

0.019

(0.
013
)

0.004

(0.027)

Growth of market share


t
-
1


0.148**

(0.
087
)

0.143

(0.
132
)

0.195

(0.
132
)

0.102

(0.
205
)

0.025

(0.0
35
)

0.134**

(0.065)

-
0.044**

(0.
021
)

-
0.056*

(0.03
3
)

Max. shareholder value


t
-
1


-
0.035

(0.
066
)

0.151

(0.
107
)

0.037

(0.
095
)

0.121

(0.
163
)

0.013

(0.0
21
)

-
0.023

(0.044)

0.014

(0.0
11
)

-
0.040

(0.03
3
)

Short
-
term profit
maximization


t
-
1

0.028

(0.080)


-
0.053

(0.130)


0.146

(0.121)


0.023

(0.175)

-
0.043

(0.027)

-
0.007

(0.054)

-
0.004

(0.015)


0.001

(0.043)


Constant


0.565


(0.930)

-
0.787

(1.027)

3.799***

(1.295)

-
0.537

(1.779)

0.397*

(0.223)

-
0.120

(0.520)

0.008

(0.096)

0.487

(0.266)

Adj. R
2

0.28

0.78

0.31

0.75

0.08

0.27

0.07

0.25

Year and Plant Fixed Effect

No

Yes

No

Yes

No

Yes

No

Yes

Num
ber of observations

445

445

445

445

445

445

445

445

Source: Interviews at 51 manufacturing establishments.

Notes: The number of observations is 445 because one year lag values are used (
-
51 observations), and also several firms were less than 10 years old

by the survey year causing the unbalanced panel (the
total number of plant
-
year observations is 496). The level of EI is the Rasch value of EI for each year. Missing values of Plant Age, Size, Niche M
arket, Growth of Market Share, and Short
-
term Profit
Ma
ximization are replaced by their mean value; dummy indicators of missing values for these variables are included in the regre
ssion to control for the effect of imputation. The estimates of these
dummy indicators are not reported for brevity. Standard error
s are reported in parentheses. *, **, and *** indicate
P
< 0.1, 0.05, and 0.01.



26


Table 7.

Markov
-
Chain average transition matrix for the numbers of programs in year
t

to year
t
+1



Y
EAR
t








YEAR
t
+1


0

1

2

3

4

5

6

7

8&9

0

0.821

0.09

0.014

0

0.02
6

0.038

0.014

0

0

1

0.048

0.698

0.19

0.032

0.016

0

0

0.016

0

2

0

0.034

0.793

0.103

0.034

0.034

0

0

0

3

0

0

0.036

0.881

0.071

0.012

0

0

0

4

0

0

0

0.058

0.788

0.096

0.038

0.019

0

5

0

0

0

0.081

0.054

0.784

0.081

0

0

6

0

0

0

0

0

0.061

0.878

0.042

0.02

7

0

0.059

0

0

0

0.059

0

0.706

0.176

8&9

0

0.05

0

0

0

0

0

0

0.95



SUMMARY STATISTICS FROM MARKOV ANALYSIS


0

1

2

3

4

5

6

7

8&9

The initial vector for 1986 is

0.24

0.22

0.10

0.14

0.10

0.02

0.12

0.04

0.04

The 1995 vector is

0.04

0.04

0.18

0.22

0.14

0.
16

0.12

0.02

0.10

The equilibrium vector is

0.02

0.04

0.10

0.26

0.15

0.14

0.14

0.03

0.12

It takes 20 iterations to reach equilibrium.


Source: Interviews at 51 manufacturing establishments.



27

Figure 1.
Scatter diagram for numbers of EI policies adopt
ed in establishments and numbers of other HR policies in establishments in 1995




Source: Interviews at 51 manufacturing establishments.


The
number
of EI in
u
se

T
otal number of other HR
practices



28

Figure 2.

The distribution of establishments by number of EI policies in 1986 and 1995


0
0.05
0.1
0.15
0.2
0.25
0.3
0
1
2
3
4
5
6
7
8
9
1986
1995
1995 predict

Source: Interviews at 51

manufacturing establishments.

Notes: No plants had all 9 EI policies in 1995. The number of plants in 1986 and 1995 is 48 and 51, respectively. “1995 predi
ct” denotes the
predicted probability of having 0
-
9 EI policies from the random draw distribution fo
r 1995.





29

Appendix Table.

Descriptive statistics of explanatory variables


Variable name

Variable Definition

Mean

Standard Deviation

Union


=1 if a plant has a union representation; =0 otherwise;

0.54



Age of plant


Age of plant in years

37.32


29.71


Log Plant Size


Log of the Number of Production Workers in a plant

7.011


0.645


Durable Manufacturing


=1 if a plant manufactures durable products; =0 otherwise;

0.645




Restructuring


=1 if a plant has recently been restructured; =0 otherwise;

0.17




Selection


=the total number of selection programs used in a plant including a detailed
screening process, personal interview, aptitude test, physical exam,

reference check, and probationary period; takes a value from 0
-
6;

4.88


1.23


Appraisal


=the

total number of performance appraisal programs used in a plant including
assessment centers, formal review sessions, and a standardized evaluation form;
takes a value from 0
-
3;

1.52


0.92


Training


=the total number of training programs used in a plant
including on
-
the
-
job
training, team building training, on
-
site training, and tuition reimbursement;
takes a value from 0
-
4;

3.36


0.65


Individual incentive pay


=1 if a plant has adopted the individual incentive pay plan; =0 otherwise;

0.55



Gain shar
ing or group bonus


=1 if a plant has adopted a gain sharing plan or group bonus program; =0
otherwise;

0.40



Profit sharing


=1 if a plant has adopted an ESOP, cash or deferred profit sharing, or employee
stock purchase plan; =0 otherwise.

0.74



Nich
e market


The degree of a plant’s focusing on niche market in the scale of 1
-
5;

3.20


1.38


Growth of market

Share

The degree of a plant’s focusing on growth of market shares in the scale of 1
-
5;

3.63


1.33


Maximizing shareholder value


The degree of a plant’s focusing on maximizing shareholder value in the scale
of 1
-
5;

3.67


1.57


Short
-
term profit maximization


The degree of a plant’s focusing on short
-
term profit maximization in the scale
of 1
-
5;

3.51


1.14