EI Adoption Termination

8/12/2019 EI Adoption Termination

1/30

ADOPTION AND TERMINATION

OF EMPLOYEE INVOLVEMENT PROGRAMS

Running titleAdoption and Termination of EI Programs

Wei Chi*

Associate ProfessorTsinghua University

Richard B. Freeman

ProfessorHarvard University and NBER

Morris M. Kleiner

ProfessorUniversity of Minnesota and NBER

Forthcoming in LABOUR 2011.

Abstract

Using an interview survey of manufacturing establishments that provide 10-years ofretrospective data on labor practices, we investigate factors associated 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 eliminate suchprograms to indicate that employee involvement does not fit in all business settings. Ourresults show that business strategy and the use of other complementary human resource

policies affect the dynamics of EI use in U.S. manufacturing establishments.


2/30

Adoption and Termination of Employee Involvement Programs

1. Introduction

Employee involvement (EI) programsthe 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 committeesare widely heralded innovations in the US. Impressed by these

programs, the federal governments Commission on the Future of Worker-Management

Relations recommended in its 1994 report that the government encourage EI programs to

improve the quality of work life and productivity (Commission on the Future of Worker-

Management Relations, 19931994).

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 that 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


3/30

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). Eaton (1994) estimates a 20 percent 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 firms 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 termination 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 relation to other human


4/30

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 1967.1We 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 Manufacturers 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 affiliate 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 administrative 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 practices through on-site visits in 1995-96 on

the notion that on-site interviews would provide more accurate data thanphone or mail

surveys The survey team interviewed general and human resource managers workers


5/30

turnover, number of employees, etc. 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, technology, 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 whether 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-managed 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 selection and staffing policieswhether the company had a

detailed screening process, personal interview, aptitude test, physical exam, reference

check, and probationary period; trainingwhether the company offered on-the-job

training, training in team building, on-site training, and tuition reimbursement;

performance appraisal policieswhether the company used assessment centers, formal

review sessions, and a standardized form to evaluate their employees periodically. The

survey also asked about financial incentive programswhether 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


6/30

niche, maximizing shareholder value of the firm, and short-term 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, would be more likely to use the EI

practice than the plants with a short-term profit focus.2We asked the senior managers to

rate the establishments emphasis on these strategies on a 1 to 5 scale with 1 indicating a

little and 5 being a great deal. The senior 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 1960s, 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 seven

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


7/30

During the interviews, we obtained considerable information about EI programs

that goes beyond the data from the structured survey. Managers 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 consultant 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 didnt deliver on quality Overall the interview process


8/30

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, and 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 governance

issues3and 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 programs

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 the

most common programs Seventy five percent of the plants in the sample had job rotation


9/30

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

correlated 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 (which 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 due 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

natural measure of similarity for vectors of 0/1 variables such as our list of EI practices


10/30

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 measure 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-plant 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 differences 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 of 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 EI 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 1995. The

numbers associated with individual programs in particular years show the number of


11/30

committee program. The figures giving total numbers of adoptions and terminations

show that in most years some firms adopt programs while others terminated them.

Consistent with the national trend in EI over the period, the number of EI policies in

place increased from 134 in 1986 to 204 in 1995a 52 percent change. This change

resulted from 112 additions of programs and 42 terminations. We also measure the

dynamics 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

years (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 of 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 observed 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%.4Between

1986 and 1995 the percentage of plants 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


12/30

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 growth 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 correct 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 establishments intensity of EI. The virtue of the Rasch model 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 characteristics:


13/30

specific policy depends on an establishments degree of EI use ( ) and the frequency

level of an EI program ( ). The function is specified to have a logistic form, giving

equation (2):

(2)exp( )

( 1)1 exp( )

i j

ij

i j

P X

, i, establishment,j, EI practice.

We use maximum likelihood to estimate the establishment parameter ( ) and the

EI policy parameter ( ).6The 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 correlated 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 tand year t+1; and whether or not the firm dropped at least one

EI program between year tand year t+1. To assess how explanatory factors affected these

decisions, we estimated the following models:

(3) 1it it i t it A X , 1it it i t it T X ,

whereAitis the measure of EI adoption in year t, and Titmeasures EI termination in year

t;Xit-1are 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


14/30

Columns 1-4 of Table 6 reports the estimated coefficients and standard errors on

variables in regressions 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 manufacturing. 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 unobserved 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, columns 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 that companies 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


15/30

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 adoption 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

seeking 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 illuminate 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 system is


16/30

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 that 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 elements 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 tand none changed their number of programs and five (possibly

different) firms had three programs in year nand 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 to 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 distribution9


17/30

over the period. As a measure of the distance between distributions, 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 equilibrate, 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 1990s (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 managers, 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 were 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


18/30

Notes

1The Census of Manufactures asks all establishments in manufacturing (over 200,000

plants) about thebusiness they conduct, geographic location, type of ownership, total

revenue, payroll, and employees. The data are confidential and only available with anapplication to the Census.

2The commonly used strategy categories in the literature are low cost, differentiation

(such as innovation or quality and service) and focus strategies (Porter, 1980; Delery andDoty, 1996; Cabrera et al., 2003). The niche market strategy we asked is similar to the

focus strategy.3They are consistent with theory and empirical approaches developed by Ichniowski

and Shaw (1995), and Ichniowski , Shaw, and Prennushi, 1997).4An alternative calculation would use a multinomial which takes account of the

difference in the frequency of each particular program.5We find a similar pattern if we examine the introduction or termination of plans.

Plants introduce 5 or 6 policies at once in 7.5% of the cases when they adopt EIprograms, whereas if they adopted plants independently, they should almost never

introduce 5-6 plans at once. Similarly, in 8% of terminations plants terminate 2 or moreprograms in 24% of the times they terminate 2 or more plants. Given the data in Table 4

both patterns are highly unlikely.6We use the Quest computer software package to estimate the Rasch measure of EIsystem.

7Estimates using probit and logit specifications showed similar results and are

available from the authors.8The 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 512 (= 29)

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 programsthe target of analysis.

9In fact, because we have so few observations 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 thetwo distributions to be the same.


19/30

References

Arthur J. (1992) The Link between Business Strategy and Industrial Relations Systems

in American Steel Minimills,Industrial and Labor Relations Review, 45(3): 488

506.

Arthur J. (1994) Effects of Human Resource Systems on Manufacturing Performance

and Turnover,Academy of Management Journal, 37(3): 670687.Banker R., Field J.M., Schroeder R.G., and Sinha K.K. (1996) Impact of Work Teams

on Manufacturing Performance: A Longitudinal Field Study,Academy of

Management Journal, 39(4): 867890.

Bartel A.P. (2004) Human Resource Management and Organizational Performance:Evidence from Retail Banking,Industrial and Labor Relations Review, 57(2):

181203.

Batt R. (2002) Managing Customer Services: Human Resource Practices, Quit Rates,and Sales Growth,Academy of Management Journal, 45(3): 587597.

Batt R., Colvin A.J.S., and Keefe J. (2002) Employee Voice, Human Resource Practices,

and Quit Rates: Evidence from the Telecommunications Industry,Industrial andLabor Relations Review, 55(4): 573594.

Berg P., Appelbaum E., Bailey T., and Kalleberg A. (1996) The Performance Effects ofModular Production in the Apparel Industry,Industrial Relations, 35(3): 356

373.Black S.E. and Lynch L.M. (2004a) What's Driving the New Economy? The Benefits of

Workplace Innovation,Economic Journal, 114: F97116.

Black S.E. and Lynch L.M. (2004b) How Workers Fare When Employers Innovate,

Industrial Relations, 43(1): 4466.

Black S.E. and Lynch L.M. (2005) Measuring Organizational Capital in the New

Economy in Corrado C., Haltiwanger J., and Sichel D. (eds.)Measuring Capitalin the New Economy,Chicago, IL: University of Chicago Press.

Cabrera E.F., Ortega J., and Cabrera A. (2003) An Exploration of Factors that InfluenceEmployee Participation in Europe,Journal of World Business, 38: 4354.

Cappelli P. and Neumark D. (2001) Do High-Performance Work Practices Improve

Establishment-Level Outcomes?Industrial and Labor Relations Review, 54(4):737775.

Commission on the Future of Worker-Management Relations (Dunlop Commission).

19931994. http://digitalcommons.ilr.cornell.edu/key_workplace/2/.Delery J.E. and Doty H.D. (1996) Modes of Theorizing in Strategic Human Resource

Management: Tests of Universalistic, Contingency, and Configurational

Performance Predictions,Academy of Management Journal, 39(4): 802835.

Drago R. (1988) Quality Circle Survival: An Exploratory Analysis,IndustrialRelations 27(3): 336 351


20/30

Freeman R.B. and Rogers J. (1999) What Workers Want,Ithaca, N.Y.: Cornell University

Press.

Gittleman M., Horrigan M., and Joyce M. (1998) Flexible Workplace Practices:Evidence from a Nationally Representative Survey,Industrial and Labor

Relations Review, 52(1): 99115.Godard J. (2004) A Critical Assessment of the High-Performance Paradigm,British

Journal of Industrial Relations, 42(2): 349378.

Goodman P.S. (1980) Realities of Improving the Quality of Work Life: Quality of WorkLife Projects in 1980, Proceedings of the Spring IRRA Meetings,Labor Law

Journal, August: 487494.

Hamming R.W. (1950) Error detecting and error correcting codes,Bell System

Technical Journal,29 (2): 147160.Hunter L.W., MacDuffie J.P., and Doucet L. (2002) What Makes Teams Take?

Employee Reactions to Work Reforms,Industrial and Labor Relations Review,55(3): 448472.

Huselid M. A. (1995) The Impact of Human Resource Management Practices on

Turnover, Productivity, and Corporate Financial Performance,Academy ofManagement Journal, 38(3): 635672.

Ichniowski C. and Shaw K. (1995) Old Dogs and New Tricks: Determinants of the

Adoption of Productivity-Enhancing Work Practices,Brookings Papers onEconomic Activity, Social Issue Microeconomics: 165.

Ichniowski C, Shaw K. and Prennushi G. (1997) The Effects of Human Resource

Management Practices on Productivity: A Study of Steel Finishing Lines,American Economic Review, 87(3): 291313.

Kleiner M.M., Leonard J.S., and Pilarski A.M. (2002) How Industrial Relations Affects

Plant Performance: The Case of Commercial Aircraft Manufacturing,Industrial

and Labor Relations Review,55(2): 195218.

Lynch L. (2007) The Adoption and Diffusion of Organizational Innovation: Evidencefor the U.S. Economy, NBER Working Paper No. 13156.

MacDuffie J.P. (1995) Human Resource Bundles and Manufacturing Performance:

Organizational Logic and Flexible Production Systems in the World Auto

Industry,Industrial and Labor Relations Review, 48(2): 197221.Osterman P. (1994) How Common Is Workplace Transformation and Who Adopts It?

Industrial and Labor Relations Review, 47(2): 173188.

Osterman P. (2000) Work Reorganization in an Era of Restructuring: Trends in

Diffusion and Effects on Employee Welfare,Industrial and Labor RelationsReview,53(2): 179196.

Pil F.K. and MacDuffie J.P. (1996) The Adoption of High-Involvement Work Practices,Industrial Relations, 35(3): 423455.

Porter M.E. (1980) Competitive Strategy, New York: Free Press.

Rankin T (1986) Integrating QWL and Collective Bargaining QWL Force 5(1): 3 6


21/30

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 System0.43

(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

Life0.31

(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)

TQM0.35

(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 Redesign0.22

(0.42)

0.22

(0.42)

0.22

(0.42)

0.22

(0.42)

0.25

(0.44)

0.29

(0.46)

0.31

(0.47)

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

Representationon 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.


22/30

21

Table 2. Correlation 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

Representation

on Board of

Directors

Selection Performance

Appraisal

Suggestion

System0.24*

Quality of work

Life0.34* 0.25*

Quality Circles 0.02 0.27* 0.30*

TQM 0.14* -0.01 0.41* 0.26*

Self-ManagedWork 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 ofDirectors

-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

Appraisal0.10* -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*


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


23/30

22

Table 3.Differences in EI use between multi-plant and single-plant companies using the metric of the number of practices that differsbetween 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


Notes: the numbers indicate the total number of differences (in 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 differences between multi-plant and single-plant companies, such as multi-plant company 1, 2,,7 versus single plantcompanies. Two of the seven multi-plant companies have a significantly smaller within-company variance in EI than the variance across the

single-plant companies; two have a significantly larger variance; the rest three are no different from single-plant companies.


24/30

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 Representation 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


Notes: The numbers associated with individual programs with minus signs give the number of firms in the specified period that terminatedprograms, while the positive numbers give the number that introduced the program.


25/30

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 5.7Joint 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 terminations over time.


26/30


27/30

26

Table 7.Markov-Chain average transition matrix for the numbers of programs in year tto year t+1

YEAR t YEAR t+1

0 1 2 3 4 5 6 7 8&9

0 0.821 0.09 0.014 0 0.026 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 06 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.



28/30

27

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


The

number

of EI in

use

Total number of other HRpractices


29/30

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 r edi ct


Notes: No plants had all 9 EI policies in 1995. The number of plants in 1986 and 1995 is 48 and 51, respectively. 1995 predict denotes the

predicted probability of having 0-9 EI policies from the random draw distribution for 1995.


30/30

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 detailedscreening 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 includingassessment 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 sharing 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 employeestock purchase plan; =0 otherwise.

0.74

Niche market The degree of a plants focusing on niche market in the scale of 1-5; 3.20 1.38

Growth of marketShare

The degree of a plants focusing on growth of market shares in the scale of 1-5; 3.63 1.33

Maximizing shareholder value The degree of a plants focusing on maximizing shareholder value in the scaleof 1-5;

3.67 1.57

Short-term profit maximization The degree of a plants focusing on short-term profit maximization in the scale

of 1-5;

3.51 1.14

Documents

EI Adoption Termination