Employee Involvement and Pay at US and Canadian Auto...

Employee Involvement and Pay at US andCanadian Auto Suppliers

Susan HelperWeatherhead School of Management

Case Western Reserve UniversityCleveland OH 44106-7208

David I LevineHaas School of Business

University of California BerkeleyBerkeley CA 94270-1500

Elliot BendolyKelly School of Business

Emory UniversityAtlanta GA 30322

We use survey data and eld research to investigate the effects of employeeinvolvement practices on outcomes for blue-collar workers in the auto-supplyindustry We nd these practices raise wages by 3ndash5 The causal mecha-nism linking involvement and wages appears to be most consistent withefciency-wage theories and least consistent with compensating differencesWe nd no evidence that employee involvement affects plantsrsquo survival oremployment growth

1 Introduction

New workplace practices such as employee involvement (EI) havebecome relatively common in American organizations (Osterman1995) largely because EI can greatly increase productivity and quality

Helper thanks the MIT International Motor Vehicle Program and the Case WesternReserve University Center for Regional Economic Issues for funding collection of sur-vey data We are grateful to the survey respondents and case-study participants RichParkin ran many of the original analyses and Laura Leetersquos input to a related articlehelped this one as well We thank Elm International Inc of East Lansing MI for gen-erously providing mailing lists and other data KC OrsquoShaughnessy and Ha Hoang pro-vided helpful comments Thanks to Steve deBrosse for help with data cleaning Thanksto David I Levine for many helpful conversations and to the Sloan Foundation fortheir continuing support of eld research by economists

copy 2002 Massachusetts Institute of TechnologyJournal of Economics amp Management Strategy Volume 11 Number 2 Summer 2002 329ndash377

330 Journal of Economics amp Management Strategy

(Ichniowski et al 1996 Black and Lynch 1997) By employee involve-ment we mean blue-collar workers using abstract reasoning skillsas well as performing manual tasks Activities using these skillsinclude meeting with a group to devise solutions to quality or safetyproblems providing suggestions for improvement and setting upmachines to make different parts These activities can occur in formalinstitutions (such as a suggestion system or pay-for-knowledge plan)or informally (See Table I)

While there is a growing consensus that EI can improve out-comes for rms few studies have examined the question of whethernew work practices can improve outcomes for employees Some authorsclaim EI helps workers by providing them with higher skills pay andquality of work life (Womack et al 1990) However others fear that EIleads to speedups lower wages less safety and more job loss (Drago1996 Parker and Slaughter 1988)

Our study examines data from US and Canadian auto suppliersto investigate whether EI has measurable effects on employeesrsquo payand on the survival of the plants in which they work In addition toquantitative analysis we report on a number of visits to plants withvery different levels of EI

In the Section 2 below we briey discuss hypotheses about theeffects of EI on compensation derived from ve standard theories fromlabor economics To aid intuition we provide examples from our eldresearch Our eld research also led us to generate several hypothe-ses that we did not derive from standard economic theories Theseare presented in Section 3 Section 4 describes the methods used totest the hypotheses survey data and case studies Section 5 providesquantitative results Section 6 describes some qualitative evidence andSection 7 concludes

2 Theories Relating Workplace Practices andEmployeesrsquo Outcomes

21 Summary of Theories

We rst summarize ve theories of why workplaces with EI mighthave different outcomes for employees than workplaces without EIhuman capital compensating differences efciency wages incentivesand complementarity and rent sharing (Groshen 1991) We also lookat a type of rent-sharing theory called management by stress For eachtheory we draw on our eld research to give examples of how EIoperates in practice Section 3 elaborates hypotheses about the factorsdriving the relationship between wages and EI

Employee Involvement and Pay at US and Canadian Auto Suppliers 331

TABLE ISummary Statistics

Abbr Description Mean Median Std Dev

Control Variable

Average age 3580 3500 642Canadian plant 014 000 034 workforce with HS diploma 7840 8500 2274log(employment) 530 535 102log(machinesproduction workers) 181 170 106 workforce male 5937 6000 2385Regional price index 104 105 009Unionized shop workers 039 000 049

Indices of Workplace Practices

Employees have completed full cycle ofimprovement process

000 038 100

Groups of workers have inuence over eachof 6 policies (work methods safety etc)

000 030 100

z(QCs present) z( employees in teams)z(teams meet on company time)

000 000 100

z(semiskilled do 5 operations such as arith-metic writing scoring daily 1 rarely 4) z(semiskilled do each of8 tasks such as setup machines inspectwork in progress)

000 000 100

Managers report that employees doimprovements managers depend onemployees and managers ask work-ers for help (1 strongly agree 5strongly disagree)

000 017 100

DIRECT Direct Involvement sum of z-transformsfor the 5 indices above

000 014 100

Labor-mgt committee exists committeemeets on company time

000 070 100

Committees amp union have inuence overeach of 6 policies (work methods safetyetc)

000 042 100

REP Representative Involvement sum of z-transforms for the 2 indices above

000 008 100

No layoffs from productivity none with-out management pay cuts

000 093 100

(continued)


TABLE I (continued)


A formal grievance plan exists highestlevel is outside arbitrator

000 008 100

Company-wide prot sharing gain sharingor team incentives or skill-based payexist (referred to below as the index ofcontingent pay)

000 027 100

Hours of formal and informal trainingmeasured separately for new hires andfor current hourly employees

000 006 100

HR Supportive HR practice = sum of z-trans-forms for the 4 indices above

000 009 100

INDEX Index of workplace transformationDIRECT REP HR

000 003 100

Outcome Variables

log(unskilled compensation) 253 251 032

log(unskilled wages) 225 221 029

Relative compensation (1 much lower 7much higher than local labor market)

470 400 137

Annual turnover 631 300 881

change in employment 1992 to 1999 (ifstill in business)

018 017 070

change in employment 1992 to 1999(including plants that went out of busi-ness)

mdash 013 mdash

Out of business by 1999 015 000 036

Note All variables with mean of 000 have been z-scored to have mean zero and standard deviation equal to unity

Human-capital theory argues that workers with higher skill levelsreceive higher compensation If high-involvement employers selectworkers with high levels of skills that are valued generally in themarket and pay them more wages and involvement are correlatedNevertheless the workers would receive the high wages even if theydid not work at a high-involvement plant

If rmsrsquo returns to EI are higher when employees have greaterrm-specic skill then involvementrsquos introduction should causewages for trained workers to rise At the same time holding all elseconstant starting wages should be bid down as workers compete forthe high post-training wages (Becker 1975) This reduction in startingwages may not be observed in the data if employers who trainalso hire more skilled or able employees Other forms of employee


compensation such as safety or bonuses would also be expected torise for trained workers

Our plant visits turned up a vivid example of how EI can be acomplement to one very general skillmdashthe ability to speak the samelanguage as onersquos coworkers and managers We visited two plants(one in California one in Massachusetts) that hired immigrant work-ers who spoke a variety of languages other than English Managersat both plants reported that while this strategy allowed them to paylower wages (in California $6ndash7 per hour rather than the $8ndash12 thatprevailed for nearby plants with native-born workforces) it compli-cated EI Workers had difculty sharing ideas with management andwith coworkers and training was more expensive In the Massachusettsplant in 1995 we observed an engineer trying to explain in English toa Vietnamese worker how to overcome a problem with a machine Theengineer at rst misunderstood the problem the operator was havingonce he did understand it took a lot of repetition and sign languageto explain the solution

Some plants have institutionalized pay for skill a pay system inwhich employees receive additional pay for each new skill they learnThese plans closely mimic the predictions of rm-specic human-capital theory (Ledford 1991) In the case study below we providequalitative evidence on the effects of one such plan

Compensating-differences theory argues that workers who faceundesirable working conditions will receive higher wages If EI requiresextra effort then plants with EI should also offer better compensationin the form of higher wages more bonuses or increased safety Ifemployees regard EI as a benet then plants that have it will offerlower wages

Our plant visits provided examples of employees regardinginvolvement as a benet as when workers made suggestions thatimproved life on the shop oor For example at Capitol Plastics inBowling Green Ohio workers on one line suggested oor mats asa means to reduce the discomfort of standing on a cement oor allday Management implemented this idea raising that linersquos quality ofwork life (MacDufe and Helper 1997)

Many employees also considered it an improvement in theirworking conditions when management implemented suggestions thatpermitted a conscientious worker to do their job better for exampleby improving the quality of parts We saw a counterexample at a (nowclosed) GM seat-parts plant in Trenton that we visited in 1990 a plantthat lacked an effective mechanism for workers to make suggestionsOne younger worker was near tears because she was spending somuch time trying to bend poorly made parts into place that she fell


behind and her hands hurt One of us overheard an older workercounseling her in the restroom not to try so hard to do a good jobif she wasnrsquot given the correct tools to do it The younger workerprotested that this wasnrsquot right that she didnrsquot want to make badparts

Compensating-differences theory also predicts that if EI requiresextra effort workers will be compensated for it Our qualitative resultshere depended on the type of EI On one hand when EI meant thatworkers had to do more tasks in the same amount of time for exam-ple check quality as well as make parts they usually believed theyshould be compensated However wages rarely rose when such taskswere added On the other hand when EI meant a substitution ofproblem-solving for production tasks we did not nd examples ofemployees saying that participating in programs such as quality cir-cles required extra effort In many cases employees were glad to havetime away from the tedium of the line

Efciency-wage theories predict that paying higher wages mayincrease workersrsquo productivity There are three main channels bywhich wages can affect productivity [Katz (1987) and Levine (1993)review this literature] A higher wage may increase worker effortdue to the greater cost to workers of losing the job so workerswant to reduce the chances that they are dismissed for low effort Ahigher wage may also increase effort by increasing workersrsquo loyaltyto the rm (Akerlof 1984) Finally a higher wage may reduce rmsrsquoturnover and recruitment costs If introducing EI increases monitoringcosts (plausible since (or because) it is harder to observe whethera worker produced a good suggestion than whether she met herproduction quota) increases the return to costly-to-measure effortandor increases employersrsquo return to worker skills and retentionthen plants with EI should pay higher wages

Our eld research did not turn up any rms that would re aworker for failure to be involved enough Several rms we visited(such as Foamade in Auburn MI) implemented a pay-for-knowledgescheme in which workers who did not achieve certication in theirjob skills by a certain date would be let go but this was a one-timetest not ongoing monitoring of work effort

Loyalty and gift exchange appeared to motivate employees inseveral cases For example at Industrial Strainer workers contributedon average half a suggestion per year each even though there was noexplicit reward for doing so When asked why several workers saidldquoItrsquos a good place to workrdquo because of above-average wages ($12 perhour rather than the $10 prevailing in the area) One worker added


that an additional motivation was ldquoManagementmdashat least some ofthemmdashcares about yourdquo

Conversely when workers felt that management did not recipro-cate their ldquogiftsrdquo of suggestions they became quite angry For exam-ple in the Trenton plant mentioned above management did not makeuse of quality-control data which employees had collected for severalmonths and did not allow the employees time to analyze the datathemselves Several employees made comments like ldquoTheyrsquore wast-ing our timerdquo or ldquoTheyrsquore making fools of usrdquo and began reportingobviously bogus quality data

Finally we came across many plants in which EI increased man-agementrsquos returns to worker skills and retention A useful comparisonis between two plants making wiring harnesses both visited in 1992One located in Mexico paid the minimum wage in its city and had100 annual turnover Apparently an informal cartel of employersrestricted management from raising wages during the employeesrsquo rstyear on the job Management there had designed a four-step trainingprocess for EI including training on working in groups problem solv-ing elementary statistics and basic quality control However evenafter two years 90 of the training they were offering was the rststage which had been repeated many times to a constantly chang-ing cast of characters Minutes of quality-circle meetings were in mostcases only one or two lines such as ldquoWe all resolved to work harderto avoid defectsrdquo Workers did not know enough about the process tosuggest countermeasures that did not depend on increased attention

In contrast a plant in Kentucky (Sumitomo Electric Wiring Sys-tems) provided extensive training to workers in statistical process con-trol and problem-solving techniques At this plant quality circles didresearch and experiments on issues such as different types of tapethat would eliminate a problem of tape slippage that exposed a wireunderneath The plantrsquos turnover rate was well below average for thearea because of its promote-from-within policy that led to high wagesfor experienced employees [even though it paid below-average start-ing wages as predicted by Lazear (1979)]

To use somewhat different language we can say that efciency-wage theories posit that EI and high wages are complementary poli-cies (Ichniowski et al 1997 Milgrom and Roberts 1995 MilgromQian and Roberts 1991) Thus rms that paid high wages for exoge-nous reasons found EI to be relatively easy to implement For exam-ple one company (Industrial Strainer) had a prolonged strike duringthe 1970s caused by managementrsquos refusal to increase wages by 5cents per hour according to the rmrsquos current manufacturing vicepresident Ever since then the rm has paid above-average wages


to avoid another costly strike The high wages made training lesscostly because turnover was low and because workers were loyal (giftexchange) Both of these factors meant that the plant achieved higherlevels of EI (as measured by numbers of suggestions and extent ofworker contributions to process improvement) for lower training costthan did other rms we observed implementing EI in the early 1990s

Incentives and complementarity The prescriptive literature onorganizational design emphasizes the importance of aligning rightsto make decisions with incentives to make good decisions Thispremise reappears in the prescriptive compensation and EI literatures(eg Lawler et al 1995) expectancy theory in psychology exchangeand work-design models of sociology and organizational behavior(eg Pfeffer 1994) and the rational models of economics agencytheory and transaction-cost economics (eg Wruck and Jensen 1994)

The move to higher EI involves substantial changes in decision-making rights as frontline employees collect and analyze more dataand suggest and implement improvements Thus we expect incen-tives in workplaces with higher levels of EI to align frontline work-ersrsquo goals with their new authoritymdashthat is to reward quality andimprovement (Milgrom and Roberts 1992 Levine 1995) Consistentwith this logic management at Foamade introduced a pay-for-skillsplan when they wanted workers to become more involved in deter-mining how to reduce defects schedule production etc

Rent-sharing theories and related theories of conict bargaining(Dow 1993) and insider-outsider relationships (Lindbeck and Snower1986) posit that worker bargaining power and the size of the rents andquasirents to be divided affect compensation These theories overlapefciency-wage theories when the employerrsquos benet of high wagesis avoiding unions (Dickens 1986) or if high prots increase the levelof wages that workers perceive to be fair (Akerlof 1984)

If employee involvement is productive then it will increase theprots of rms that adopt it Then wages will increase if rms sharethese gains with workers either because it seems fair to do so orbecause workersrsquo bargaining power does not decrease as a result of EI

EI can increase worker bargaining power by increasing workersrsquofeeling of solidarity due to increased interaction Involvement mightalso increase workersrsquo rm-specic knowledge which can make it dif-cult to replace workers and makes rms rely upon senior workers totrain new employees Involvement might also make it more difcult tomonitor workersrsquo actions so that high productivity increasingly relieson worker cooperation Finally involvement might make it easier foremployees to disrupt production so that worker noncooperation orother reactions to perceived unfairness is more costly to the rm


In addition to these channels we saw in our eldwork an exam-ple of EI facilitating union-organizing efforts At a Japanese-ownedstamping plant near Detroit pro-union workers used shbone dia-grams and other tools they had learned in their problem-solving train-ing to prioritize issues and brainstorm solutions to problems thatarose during their ultimately successful 1995 organizing drive andsubsequent strike Workers from the nearby Mazda plant who hadreceived similar training assisted employees at the supplier plant Thisincreased cohesiveness came largely from increased skillsmdasha differentargument (though complementary) to that made by many sociolo-gists who emphasize how increased feelings of solidarity can increaseworkersrsquo bargaining power

Our eldwork provided cases where EI had both positive andnegative effects on employer monitoring and turnover costs On theone hand EI added higher-level problem solving to workersrsquo jobswhich is harder to monitor and more skill-intensive than working onan assembly line (as in the Industrial Strainer and Foamade examplesabove)

Management by stress however sometimes also reduced employ-eesrsquo bargaining power by codifying workersrsquo tacit knowledge and byreducing unionsrsquo power (Parker and Slaughter 1988 1988 Sheahanet al 1996)

One of the goals of many EI programs was to codify work-ersrsquo tacit knowledge Creating standardized worksheets (sometimesreferred to as ISO 9000 worksheets) could increase productivity bydiffusing best practice across employees and by making the produc-tion practices susceptible to systematic improvement (Adler 1993)However these ldquoscab sheetsrdquo (as some union activists call them) alsomade it easier to replace trained workers with relatively unskilledones to move work to lower-wage locations and to replace workerswho struck In this case EI in one plant could reduce wages at otherplants

For example in several instances workers reported being video-taped or writing detailed descriptions of how they did their jobsmanagement then transferred work to other plants that paid lowerwages In one example a wiring-harness maker in Ohio transferredknowledge to plants in China and Mexico Another company aptlynamed Federal Screw Works used ISO 9000 work instruction sheetsgenerated by its Detroit workforce to start a new lower-wage plant inrural Michigan At both companies management then used the threatof moving even more work to the new plants to bargain for lowerwages at the old plants


Second managers can use EI to reduce union power For exam-ple managers can respond more quickly to problems raised throughEI channels rather than through union channels thus decreasingworkersrsquo perceptions of union effectiveness [See Parker 1985 andParker and Slaughter (1988) for examples] Management can createnew positions for line workers such as team leader or quality coor-dinator that offer highly motivated individuals an opportunity toadvance and to feel that they are helping fellow workers without thehassle faced by union ofcers of having to win election and reelection

In nonunion workplaces the presence of management-sponsoredproblem-solving channels can reduce employeesrsquo dissatisfaction Tothe extent the lower dissatisfaction reduces the need for compen-sating differences andor reduces the threat of unionization wagescan also decline at nonunion workplaces These effects are hard forresearchers to observe since it is usually difcult to gain permis-sion from management to visit plants with undesirable workingconditions

22 Basic Hypotheses

The rst four theories discussed above provide a justication for ourrst hypothesis

H1A Wages will be higher for workplaces with substantial employeeinvolvement than for traditional workplaces

Theories of rm-specic training efciency wages and rentsharing but not compensating differences also imply that high-involvement workplaces should have lower levels of voluntary turn-over (quits)

H1B Turnover will be lower in workplaces with high levels ofemployee involvement or training

The management-by-stress theory by contrast suggests1

H1C Wages will be lower and turnover higher for workplaces withemployee involvement than for traditional workplaces

1 When EI is a benet to employees rather than a cost the theory of compensatingdifferences suggests the same hypothesis about wages but for almost opposite rea-sons That is rather than being a management innovation that shifts worker bargainingpower down EI is seen as a factor that improves working conditions


23 Disentangling the Theories

In the theories above the effects of EI work through several differ-ent channels We can attempt to disentangle the relative importanceof the effects predicted by the several theories by controlling for theintervening variables We can also investigate the possibility that theeffects of EI might be different for different types of plants (those withdifferent work organizations management strategies etc) Howevergiven the limitations of both data and theory this is a difcult taskand we will attempt not to overstate our conclusions (Data problemsinclude unobserved heterogeneity) A theoretical issue was noted by areferee who pointed out that many of the hypotheses we posit aboutthe effect of EI on wages involve adding a third variable (call it Z)to the wage equation If Z is endogenously determined then whetheradding Z to the wage equation increases or decreases the effect of EIon wages often depends on why Z is varying This is an importantpoint and we provide some examples below However the causalchannels we emphasize are those most prominent in the literature

Human-capital theory suggests that any link between wages andEI is largely due to the increased returns to skill when EI is presentParticularly if the rm has not hired new workers since it started EIthe increased skill will largely be obtained through training If thetraining is rm-specic or has already been completed then employ-ers will raise wages to make sure they retain access to the humancapital they have created This logic leads to

HumanCapitalA Training should be correlated with high levels ofemployee involvement

and

HumanCapitalB Controlling for the existing stock of training shouldlargely eliminate the relation between employee involvement andwages2

When some of the skills are rm-specic human-capital theoryalso implies that lower turnover should accompany high levels oftraining because if workers leave then neither workers nor the rmbenets from the investment in training Thus we have

HumanCapitalC Firms that have more employee involvement shouldhave less turnover

2 However wages should be negatively related to the ow of current nonspecictraining because workers are investing in their human capital


Compensating-differences theory suggests that rms that introducenew forms of employee benets along with EI will have a smallerincrease in wages than rms that do not provide such benetsThat is

CompensatingDifferencesA Including performance pay and no-layoffpolicies in the wage equation will signicantly reduce the coefcienton employee involvement3

Efciency wages If the relationship between work organizationand turnover is due to efciency wages then we have

EfciencyWagesA Wages relative to the local price level and wagesrelative to regional averages should correlate negatively withturnover

and

EfciencyWagesB Controlling for wages should largely eliminate therelation between employee involvement and turnover

Each of the efciency-wage variants leads to different views ofthe links between EI and wages If the link between involvement andwages is largely due to gift exchange then employee loyalty shouldbe higher at workplaces with high levels of EI This loyalty shouldlead managers to perceive that employees are more willing to takeactions beyond mechanically producing parts This leads to

GiftExchangeA Managers at workplaces with more EI should reportthat they have fewer supervisors per worker and that employees helpout in ways not specied in their job description are less likely to takeadvantage of management and are less reluctant to share ideas withmanagement

and

GiftExchangeB Controlling for these proxies for employee loyaltylargely eliminates the relation between employee involvement andwages

3 As a referee pointed out employees would be more likely to demand no-layoffpolicies in industries with low employment growth If the policies were not 100 effec-tive employees might still not want to participate in EI because of their fear of lay-offs due to productivity-enhancing suggestions So it is possible that in equilibriumone would observe low EI being correlated with no-layoff policies However indus-tries with low employment growth typically also have declining employee bargainingpower suggesting that employersrsquo higher costs of a no-layoff policy in such circum-stances would inuence the outcome more than would employeesrsquo higher valuation


The turnover-cost version of the efciency-wage theory implies

TurnoverCostA Including measures of turnover cost such as trainingreduces the coefcient on employee involvement in the wage equa-tion

This hypothesis is the same as HumanCapitalBIncentives and complementarity Because workplaces with more EI

depend more on employeesrsquo incentives to take initiative the theoryof complementarities between involvement and incentives implies

IncentiveA Pay practices such as gain sharing and prot sharing willbe more common in plants with higher levels of employee involve-ment

When employees provide ideas that increase productivity com-panies are sometimes left with excess employment They have thusan incentive to lay off excess labor These layoffs in turn will loweremployeesrsquo incentives to generate new ideas Thus we expect EI tobe most successful when it is coupled with policies that limit layoffsdue to new ideas (Levine and Parkin 1996) This complementarity inturn yields the hypothesis

IncentivesB Policies limiting layoffs due to employee suggestionswill be more common in plants with higher levels of employeeinvolvement

3 Inductively Based Hypotheses

In addition to providing intuition for the theoretically generatedhypotheses above our eldwork also helped us generate somehypotheses that are not highlighted in the general economics litera-ture Most importantly our respondents repeatedly emphasized thatwhen they perceived that EI increased employment security they foundthis a major incentive to participate and vice versa As mentionedabove in the discussion of compensating differentials policies such aspromote-from-within (Sumitomo) and job security in the face of pro-ductivity improvements (Industrial Strainer) can provide incentivesfor participation in EI4

Job security was a particularly important motivator for work-ers in our sector for several reasons Most auto-supply plants paid

4 Conversely a guarantee of lifetime employment regardless of what happens tosales removes the incentive to participate to keep onersquos plant open Only one rm wevisited had such a policy


above-market wages unemployment was fairly high in most partsof the nation at the time of the survey (1993) and many workershad painful memories of the very high unemployment of the early1980s and workers often valued friendships with coworkers Thusemployment security is an important component of the welfare ofsemiskilled workers as a whole a component that is missed if welook only at wages For example EI would be good for workers ata factory even if it lowered wages slightly as long as it also raisedemployment security For these reasons we analyzed another set ofdependent variables plant survival to 1999 and employment growthbetween 1993 and 1999

EI might increase a plantrsquos employment and its probability ofsurvival for several reasons To the extent that EI improves the plantrsquosperformance it should increase prots and market share for the plantIf the management-by-stress theorists are correct and EI lowers work-ersrsquo bargaining power and wages then EI would raise labor demand(unless the productivity gains outweighed any increase in marketshare) Thus we have

SurvivalA Plants with more employee involvement in 1992 haveincreased employment growth and increased probability of survivalfrom 1992 to 1998

It is also possible that we will observe a spurious positive cor-relation if employee involvement is introduced when companies arefeeling ush and want to do something nice for workers (eg toreduce their alienation) However we did not nd any examples ofprograms being introduced for these reasons our respondents indi-cated that their introduction of the programs was generated by theprot motive ldquoWe do this to increase qualityrdquo and ldquoto help us com-pete with the Japaneserdquo were common explanations of the rationalefor introducing EI [For similar ndings see Kochan et al (1984)] Inany case the early 1990s (when the data were collected and whenmost EI programs were starting) was a time of shrinking sales andlow margins

Alternatively EI may lower a plantrsquos employment and proba-bility of survival If EI helps managers extract high-wage workersrsquoknowledge then it increases the ability of the rm to establish a newlower-wage plant elsewhere Moreover if EI increases productivitywithout increasing demand for the product workers may be maderedundant This effect reduces employment but raises the probability


of survival These arguments lead to

SurvivalB Plants with more EI in 1992 have decreased employmentgrowth and decreased probability of survival from 1992 to 1998

We could also nd a spurious negative correlation betweeninvolvement and employment changes if EI is introduced whensurvival is threatened

4 Data and Methods

We rst discuss our qualitative methods and then the quantitativemethods and data

41 Qualitative Methods

In this paper we use evidence from plant visits in a variety of ways(Eisenhardt 1989) to facilitate intuition in generating hypotheses totest hypotheses and to facilitate intuition in describing results Thecases used in generating the hypotheses above were drawn fromapproximately 60 auto-supplier plants visited by Helper and collab-orators over the past 10 years In most cases plants were chosenbased on convenience or the needs of other research The visits lastedfrom four hours to three days and always included a plant tour Inthese visits we (at least two interviewers were always present) spokewith two to fteen people ranging from company president to lineworker In about a dozen cases we had the opportunity to interviewworkers for at least 30 minutes without the presence of managementAll but three of these cases were union plants We took extensivenotes and cross-checked them with the other interviewers and (inabout half of the cases) with the interviewees All interviewees werepromised condentiality where real names are used it is with inter-vieweesrsquo permission Some of the cases have been written up moreextensively elsewhere for the wiring-harness case see Helper (1998)for Foamade and Industrial Strainer see Sheahan et al (1996) andHelper (1999) for Sumitomo see MacDufe and Helper (1999)

In addition to the large convenience sample of plants we con-ducted a case study as a quasinatural experiment (See Section 6)

411 Questionnaires In 1993 Helper conducted two sur-veys of automotive suppliers The rst survey was sent to the divi-sional director of marketing at automotive suppliers in the UnitedStates and Canada This questionnaire asked about relationships with


customers and product characteristics The second survey was sentto plant managers and asked about operations policies and relation-ships with workers Each answered the questionnaire for their mostimportant customer regarding one product that was typical of theirbusiness unitrsquos output

A questionnaire was sent to every automotive supplier andautomaker component division named in the Elm Guide to AutomotiveSourcing (available from Elm Inc in East Lansing Michigan) Thisguide lists the major rst-tier suppliers to manufacturers of cars andlight trucks in the United States and Canada The response rate was55 for the sales-manager survey and 30 for the plant-managersurvey

Survey respondents were representative of the population inrm size and location as compared with data from the Elm Guideand from County Business Patterns for SICs 3714 (automotive parts)and 3465 (automotive stampings) However vertically integrated busi-ness units of the automakers were underrepresented The respondentsaveraged 18 years in the automobile industry and 11 years with theircompany

412 Employment Growth and Survival Data on 1999employment came from the 1999 Elm Guide we matched this infor-mation to the 1993 survey data If we could not nd a plant in the1999 Guide we called the plant to conrm that it was indeed out ofbusiness (that is its phone was disconnected or had been assigned toanother user) If a plant had been sold to another rm we counted itas continuing to exist We were able to account for all plants as eithersurviving or out of business However the 1999 employment gureswere often missing from the Elm data

42 Variable Construction

421 Measuring Employee Outcomes Our wage measureis the average wage (not including benets) at the plant for unskilledand semiskilled employees We also used a measure of total compen-sation equal to this wage measure times 1 plus the percentage of totalpayroll devoted to benets (not including retiree benets)

For robustness we compared results with a measure of compen-sation relative to the local labor market Specically we asked howtotal compensation (including benets) for unskilled and semiskilledproduction workers compared with that received by equivalent work-ers in all industries in the respondentrsquos area This was a seven-pointqualitative scale ranging from 1 (ldquomore than 20 lowerrdquo) to 7 (ldquomorethan 20 higherrdquo)


The measure of turnover in the dataset combines quits and lay-offs Layoffs can avoid the need to re low performers Moreoverwhen employees can select to be laid off layoffs reduce quits If lay-offs at one plant are correlated with layoffs at other potential employ-ers fear can reduce the desire to quit For all of these reasons theanalysis of turnover is most convincing in workplaces where somehiring occurred last year we emphasize results from this sample

422 Identifying Plants with Employee InvolvementThe survey measured workplace practices ranging from union-management committees to employees maintaining their own machinesto the presence of problem-solving groups Given the large number ofmeasures the appropriate way to measure ldquoemployee involvementrdquois not obvious We used four techniques some based extensively ontheory and others driven by the data Each procedure has advan-tages and disadvantages so we emphasize results robust to multiplemethods

4221 Theory-Motivated Index The theory of complementary work-place practices suggests EI is most likely to succeed when it coupleshigh levels of direct EI with representative participation and with sup-portive workplace practices ranging from the rule of law to high lev-els of training to appropriate incentive pay plans (Lawler et al 1995Levine 1995) A workplace with all of these practices looks quite dif-ferent from a traditional hierarchical factory Because of the sweepingchanges it measures we call the index that includes direct involve-ment representative participation and supportive practices the indexof workplace transformation The most theory-driven method we usedto develop this measure built up indices of direct and representa-tive participation as well as indices of the theoretically appropriatehuman resources (HR) polices This subsection outlines each of thesesubindices

Direct participation The subindex of direct participation was con-structed as a z-score of four components Each component is z-scoredbefore summing or use in a regression

One component measured the number of tasks performed byfrontline employees It was the sum of whether semiskilled employ-ees do each of ve operations such as write paragraphs or do arith-metic or solve problems in a group scoring daily 4 weekly3 monthly 2 and rarely 1 (z-scored) plus the proportion ofeight tasks performed at the plant such as setting up machines andusing quality assurance data to recommend improvements done bysemiskilled workers (z-scored)


The second component measured whether groups of workershave inuence over six policies such as work methods and taskassignments purchasing new tools and safety and health policies (z-scored) The third component measured use of teams It was the sumof three z-scored items whether quality circles or similar groups werepresent the proportion of employees in such groups and whetherthe groups met on company time

A one-item index measured whether at least one group of work-ers had completed a full cycle of a formalized improvement processsuch as the plan-do-check-act cycle of Deming

Finally an index of whether managers report listening toemployees was the sum of three attitude questions ldquoEach year weexpect our shop workers to make substantial improvements in theirown method of operationsrdquo ldquoOur plantrsquos performance depends cru-cially on the active cooperation of our unskilled and semiskilledworkersrdquo and ldquoWe frequently ask workers at our plant to help us inways not specied in their job descriptionrdquo These were coded from1 for ldquostrongly disagreerdquo to 5 for ldquostrongly agreerdquo

Representative participation The index of representative partici-pation was the sum of two (z-scored) components One measuredwhether joint labor-management committees andor a union haveinuence over each of six policies as listed in the direct-inuencescale The second component was the sum of a dummy 1 if labor-management committees existed and a dummy 1 if such committeesmet on company time

Supportive HR practices We examined four supportive HR prac-tices grievance procedures new pay practices training and employ-ment security practices Each subindex was z-scored

The grievance subindex was the sum of a dummy for havinga grievance procedure plus a dummy for having appeals to neutralarbitration

The new-pay-practice subindex was the sum of dummies forwhether there were compensation plans for unskilled workers includ-ing company-wide prot sharing gain sharing team incentives andskill-based pay

The training subindex was the sum of z-scored hours of formalinformal training of new hires plus z-scored hours of formal informaltraining of workers with at least one year of experience

The subindex of employment security practices was the sum oftwo dummies for agreement that the company has made a commit-ment to its regular work force that (1) ldquono layoffs will result fromproductivity increasesrdquo and (2) ldquothere will be no layoffs unless therehave also been pay cuts for managementrdquo


Summary index The summary index of EI was the sum of theabove three (z-scored) indices direct participation representative par-ticipation and HR policies

The strength of this index is that it is derived from prior theory(Levine and Tyson 1990) Because the questionnaire and this indexwere both constructed based on this theory and before the data werecollected this index avoids data mining Moreover the results can beexpressed in a parsimonious fashion The downside of this index isthat it imposes strong functional-form restrictions on the data restric-tions that are not in fact supported Implicitly this method assumeseach subindex is equally important and assumes they are substitutesthat is a one-standard deviation increase in training will have thesame effect as a one-standard-deviation increase in the number oftasks that line workers perform

4222 Multiple Subindices of Employee Involvement We also enteredthe three main subindices independently This test has most of theadvantages of the theory-driven single index yet relaxes its strongfunctional-form restrictions The downside of this method is that theindices are multicollinear Thus individual coefcients are difcult tointerpret

4223 Individual Subindices of Workplace Practices Next we enteredeach of the three main subindices one at a time In this method eachsubindex picks up the effects of all HR practices correlated with itspresence

4224 Cluster Analysis Our nal method cluster analysis permitsthe data to speak about how to reduce the dimensionality of work-place practices Cluster analysis identies workplaces with similarpractices This technique has been widely used in management andeconomic research to identify meaningful subgroups of populations(Bearse et al 1997 Henriques and Sadorsky 1999 Sarasvathy et al1998) Dummies representing these clusters are then entered into thewage equation

A meaningful set of clusters has low within-group variance andhigh between-group variance For any given number of clusters thecentroid method of cluster analysis we use maximizes the ratio ofwithin- to between-group variance on the 11 work-index subindicesdescribed above This method uses a Euclidean metric to evaluate thedistance between clusters

In cluster analysis one must choose the number of clusters aproblem with no single best solution If the number of clusters is cho-sen appropriately then when we add an additional cluster it will be


near (in some metric) to the existing clusters To choose the appro-priate number of clusters we started with two clusters per union onnonunion subgroup Our statistical tests showed that this was a goodsolution5 In any case results (not shown) results with three clustersper subgroup were similar to the results with two

423 Baseline Wage and Turnover Equations Table Ipresents summary statistics

All regressions control for the workforcersquos average age percent-age of male and percentage of high-school graduates We includedummies for unionized plants and whether the plant is located inCanada We controlled for the regional price index6 the log of employ-ment and the capital-labor ratio7 The survey data also have an exten-sive set of controls for product and process characteristics these werenever signicant so we omitted them to preserve degrees of freedom

The baseline wage regression [Table II column (1)] has theexpected signs Plants with a higher proportion of men with a higherproportion of high-school graduates with an older workforce witha union and with more employees pay higher wages Somewhatsurprisingly the regional price index does not have a statisticallysignicant effect on the wage level

The baseline results on turnover are in Table II column (4) Asexpected high education large plant size and union status predictlower turnover These effects are economically large although onlythe rst is statistically signicant

5 Calinski and Harabasz (1974) noted that if a cluster is distant from k 1 otherclusters then the k 1 logistic equations predicting cluster membership for each pairshould have high explanatory power and a high P-value for the chi-squared statisticFor each subgroup we ran a logistic regression permitting 11 workplace practices topredict cluster membership We then performed the cluster analysis with three clustersfor each of the union and nonunion subgroups We ran a logistic regression predictingcluster membership for each pair of clusters leading to three logits for each subgroupWe then compared the geometric mean of the three P-values from the chi-squared teston the logit with the P-value of the logit when we had only two clusters per subgroupIn both cases the geometric-mean P-value when we added the third cluster was smallerthan the P-value of the logit with only two We also redid the analysis for the datasetas a whole (not separating union and nonunion plants) and obtained similar resultsSee also Milligan (1980) Milligan Cooper (1985)

6 For plants located in US metropolitan areas we used the American Chamberof Commerce Researchers Association Cost of Living Index for plants outside USmetropolitan areas we used the mean for nonmetropolitan areas for Canadian plantswe used purchasing-power parity estimates For more detail see Helper and Parkin(1995) We are grateful to Richard Parkin for calculating these numbers

7 While most capital-labor ratios use the value of capital we use the number ofmachines per production worker


TA

BL

EII

B

as

el

ine

Wa

ge

Eq

uatio

ns

(1)

(2)

(3)

(4)

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nsk

illed

wag

es)

ln(u

nski

lled

com

pen

sati

on)

Rel

ativ

eC

omp

ensa

tion

Lab

orTu

rnov

erR

ate

BSE

BSE

BSE

BSE

(Con

stan

t)0

859

018

20

968

021

21

266

121

327

957

806

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vera

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e0

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20

034

001

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295

004

50

039

025

23

819

166

0

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kfor

cew

ith

HS

dip

lom

a0

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kers

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ple

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n31

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5 Quantitative Results

51 Incidence

511 Regressions Table III(A) presents regressions on theincidence of employee involvement The index of direct EI is higherin plants that were large Canadian had younger workers and hadmore employees with a high-school degree Machines per workerand the proportion male or union of the workforce did not havea statistically signicant effect on the level of involvement [column(1)] Employment security and representative involvement were alsopredictors of direct involvement [columns (3) and (5)] Column (2)shows overall impact of adding training (not controlling for unionstatus) and column (4) shows different ways of conceptualizingunionnonunion differences in EI incidence

512 The Cluster Analysis The cluster analysis also showsthat high-involvement practices were correlated supporting theoriesof complementarities [Table II(B)] The union workplaces were morelikely to have grievance procedures had more representative partic-ipation and had lower levels of nontraditional forms of incentivepay Our ANOVA tests show that the clusters we call high-involvementhad statistically signicantly higher levels of most of the involvementpractices than did the low-involvement clusters

The above results describe the differences in workplace practicethat the cluster analysis used to dene the clusters It is importantto know how these clusters differ along other dimensions The clus-ters did not differ signicantly on size location or the proportionof the workforce that was male or that had a high-school degreeIn the nonunion but not the union subsample plants in the high-involvement cluster were a bit newer and had a slightly youngerworkforce In short the employers and employees in the high- andlow-involvement clusters within each subsample were rather similar

Robustness checks When we added a third cluster per union ornonunion subsector the third cluster was always small (fewer than16 workplaces) Moreover the third cluster was intermediate in bothworkplace practices and compensation Thus our results with clusteranalysis are robust to alternative clustering techniques

52 Employee Involvement and Wages

The basic result of the paper is in Table IV column (1) A one-standard-deviation rise in the summary index of EI is correlated witha wage increase of 36 at nonunion rms and of 52 at union


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dex

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wit

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nio

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lesi

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Sign

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rms8 These results support the hypothesis H1A (human-capitaltheory efciency wages and rent sharing) but not H1C (manage-ment by stress) If we examine compensation (wages benets) theresults are similar in magnitude although not statistically signicant[column (2)]

Similarly we can analyze managersrsquo reports of compensation rel-ative to the local labor market [column (3)] A one-standard-deviationrise in the index of employee involvement increases relative compen-sation about 024 points (averaging union and nonunion effects) Aone-point move is equal to changing from about 4ndash9 higher to 10ndash20 higher than compensation received by all workers in the areathus a 02-point move corresponds to perhaps a 2 move in wagesmdashnot too far from the 35 estimated in column (1)

521 Results with Less Aggregated Workplace Prac-tices We also examined which components (direct involvementrepresentative involvement or supportive HR practices) drove thepositive correlation with wages (Table V) The index of direct involve-ment has the largest effect in nonunion workplaces but no effectin union workplaces The index of representative involvement incontrast has the largest effect in union workplaces but no effect innonunion workplaces The differences in coefcients while large arenot statistically signicant

522 Results with Clusters In the union subsamplethe high-involvement cluster paid average production-worker wagesabout 55 above the more traditional cluster this gap was not signif-icant [Table III(B)] In the nonunion subsample the high-involvementcluster paid average production worker wages about 63 above themore traditional cluster (P lt 010) For other variables gaps in cellmedians between EI and non-EI plants were bigger for union thanfor nonunion plants but always had the same sign

In the wage equation with standard controls the high-involvement nonunion cluster paid average production worker wagesalmost 9 above the more traditional nonunion cluster (P lt 001)while the union high-involvement cluster paid average productionworker wages about 45 above the more traditional union cluster[difference not signicant Table VI column (1)] These effects wereslightly larger for compensation than for wages [column (2)] andsmaller and not signicant for the relative compensation measure[column (3)]

8 We tested throughout for differences in coefcients between union and nonunionplants these differences were never statistically signicant

358 Journal of Economics amp Management StrategyTA

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523 Overview of Results on Wages and EmployeeInvolvement In short the results paint a fairly consistent pictureof a small but usually statistically signicant effect of new work-place practices on wages or compensation The results were robustto various measures of EI9 We did not nd statistically signicantdifferences in coefcients between union and nonunion plants

524 Turnover and Employee Involvement With stan-dard controls turnover is estimated to be essentially uncorrelatedwith workplace practices [Table IV column (4)]10 Thus the turnoverresults do not support the hypothesis that specic human capitalrent-sharing or efciency wages are the reason that high-involvementemployers pay higher wages

53 Disentangling (Some of) the Theories

531 Human-Capital Theory The theory of human capitalsuggests that any apparent relation between wages and EI is actuallydue to the relation between wages and employee skill Our compositemeasure of EI then is picking up skills in two fashions Most directlya measure of training (hours of training received last year per worker)is included in the index Moreover human-capital theory interpretsmany EI practices as requiring higher skills such as problem-solvingor equipment maintenance

We nd no strong support for the predictions of human-capitaltheory First in contrast to the hypothesis HumanCapitalA ourmeasure of frontline EI is almost uncorrelated with our measureof training [Table VII(A) column (1)] We also nd no support forthe hypothesis that controlling for training largely eliminates therelation between EI and wages [HumanCapitalB Table VII columns(2ndash4)] Specically when we add training to the wage equationthe coefcient on training is small and insignicant Furthermoreadding training does not change the coefcient on direct EI by aneconomically or statistically signicant amount11

9 In addition to the results presented here we also found signicant wage increaseswith employee involvement when EI was measured using factor analysis and whenworker suggestions were included as a measure of EI The results presented in thispaper concern wages of semiskilled or unskilled employees We also looked at the effectof EI practices on the wages of skilled blue-collar workers we found such workers alsoearned 3ndash5 more at plants with EI

10 If the subindices are entered separately each one is uncorrelated with turnover(results not shown)

11 As noted above the hypotheses concerning human-capital theory hold if traininghas already been completed This stock assumption would seem most valid for plantsthat had only experienced employees since their past training would be large relativeto the current ow of training Thus we reran the regression including only plants thathad not hired in the last year Still we found that training did not reduce the coefcienton EI in fact this coefcient increased slightly [Table VII(A) Panel 2 columns 1ndash3]


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orce

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Thus training does not seem to explain the wage increases asso-ciated with EI12 This nding also casts doubt on the idea that rmspay efciency wages to offset increased turnover costs after trainingfor EI (TurnoverCostA) Finally we did not nd that EI predicts lowerturnover contradicting HumanCapitalC

532 Compensating Differences When we added mea-sures of supportive HR practices to our regressions we found thatthe coefcient on our measures of EI changed very little in the wageequation contradicting the compensating-differences theoryrsquos predic-tion that adding bonuses or a no-layoff policy would reduce wages[See Table V(A)]

533 Ef ciency Wages Consistent with efciency-wage the-ories wages that are high relative to the local price level or to theregion predict low turnover (EfciencyWageA) A 32 rise in the plantaverage compensation for unskilled employees (1 standard deviation)reduces turnover by about 3 percentage points per year This effect isabout half the mean or a third of the standard deviation of turnoverrates across plants [Table VII(B) rst panel]

In addition we found that rms with more supervisors perworker had both less involvement and lower wages [Table VII(B)second panel columns (1) and (2)] This result is consistent withgift-exchange versions of efciency wages (eg workers feel man-agement trusts them more if there are fewer supervisors makingworkers more willing to provide the ldquogiftrdquo of involvement) It is alsoconsistent with monitoring-cost versions (supervisors are a substitutefor a high cost of job loss in generating effort)

We further investigated the gift-exchange hypothesis by usinga two-item measure of managersrsquo perception of employee loyaltyThe extent to which managers report that workers ldquomight takeunfair advantage of managementrdquo and that ldquoworkers sometimesfeel reluctant to share their ideasrdquo (1ndash5 scales) Consistent with gift-exchange theory plants with more loyal employees (as perceivedby the employer) had more EI A one-standard-deviation rise in

12 Another human-capital story would be that when rms introduced EI they alsohired new more highly skilled workers and paid them more However that storydoes not apply to this industry It is true that plants with a higher percentage of high-school graduates had more involvement but the workforces did not change when EIwas implemented Our impression from plant visits is that most of the EI programswere only a few years old in 1993 (Unfortunately we didnrsquot ask this in the survey)This period was characterized by low turnover almost all plants reported that theirturnover rates (only 36 on average in 1993) had changed little since 1989 The periodwas also characterized by little hiring 27 of respondents had not hired productionworkers in the year preceding the survey


perceived employee loyalty predicted 10 higher direct EI (resultsavailable on request standard controls were included)

This measure of gift exchange does not appear to drive the wage-involvement correlation Including the two-item index of loyalty inthe wage equation had no effect on the index of direct EIrsquos coefcienton wages (results available on request) Thus we have support forthe hypothesis GiftExchangeA but not GiftExchangeB [Table VII(B)column (3)]

Inconsistent with efciency-wage theories EI does not predictlow turnover so there is no relationship for wages to mediate (con-tradicting EfciencyWageB) However more training does predictmarginally lower turnover Consistent with EfciencyWageB thistraining effect goes away when wages are controlled for [Table VII(B)rst panel columns (1) and (2)]

534 Incentives and Complementarity Consistent withthe hypothesis IncentiveA pay practices such as gain sharing andprot sharing were more common in plants with higher levels ofEI In a noncausal regression a one-standard-deviation-higher useof new pay practices predicted 011-higher standard deviations onthe index of direct EI (standard controls results available uponrequest)

Also consistent with the theory of complementarity and the needfor employee incentives high-involvement plants had policies limit-ing layoffs due to employee suggestions (hypothesis IncentivesB) Thecluster analyses also found that high-involvement plants had above-average employment security and new pay practices [Table III(B)]The various subindices of EI were positively correlated as predictedby theories of complementarities [Table I(B)]

535 Management by Stress As noted above we did notnd quantitative support for the predictions of management-by-stresstheorists that wages at plants with EI would be lower However wedid nd some support for the management-by-stress view that unionparticipation is important for workers to win high wages at union-ized plants In union plants wages rise only if the plant has high rep-resentative involvement and the effect of direct involvement aloneon wages was negative (and in some specications signicant) forunion plants In nonunion plants workers did receive higher wagesif they had direct involvement but not enough to overcome the union-nonunion pay gap (See Table V rst panel)


We were unable to test the theoryrsquos hypotheses about EIat one plant affecting wages and employment at other plantsbecause we lack data on transfer of information from one sup-plier plant to another and data on plants outside the US andCanada

54 Plant Survival and Employment Change

We had two measures of employment growth the indicator variablefor plant survival between 1993 and 1999 and the percentage changein employment

The odds of plant closure were essentially identical for theunion high- and low-involvement clusters and for the nonunionhigh-involvement cluster ranging from 16 to 18 (differences notsignicant) In contrast only 9 of the nonunion low-involvementcluster went out of business (different from the other three clusters atP lt 01)

The median employment rise (coding plant closure as a verylarge decline) was 24 at the low-involvement nonunion clusterwhich was statistically signicantly different from the 9 rise at thehigh-involvement nonunion cluster Both were statistically signi-cantly higher than the near-zero median change at the two unionclusters

In regression results involvement in 1993 did not predicta plantrsquos staying in business until 1999 [Table VIII column (1)]Moreover in the limited sample with data on employment in 1999employee involvement in 1993 did not predict employment growth(Table VIII) In results not shown these ndings were robust tovarious measures of workplace practices in 1993

These results suggest high-involvement workplaces do not savejobs at US auto suppliers consistent with the hypothesis SurvivalBand contradicting SurvivalA

One possible explanation for the lack of employment growth athigh-involvement plants is that EI raises productivity but that productdemand is inelastic so labor demand often declines In this scenario EIshould predict fewer plant closings even at low employment growthIn fact the data do not exhibit this pattern

Alternatively it may be that plants that faced nancial difcul-ties in 1992 were more likely to adopt more employee involvementbut the benets of employee involvement did not outweigh thebad conditions that led to its adoption Again the data are notconsistent with this interpretation Managers in high-involvementplants were less not more likely to report their plants had had


TA

BL

EV

III

Eff

ec

tso

fE

mpl

oy

ee

Inv

ol

ve

me

nt

on

Em

plo

ym

en

tG

ro

wth

1

99

3ndash1

99

9

(1)

(2)

(3)

OL

SE

mpl

oym

ent

1999

OL

SE

mp

loym

ent

1999

Log

itS

urv

ival

Sam

ple

Fil

ms

Stil

lin

Bu

sine

ssFu

llSa

mp

leFu

llSa

mp

lein

2000

BSE

DP

DX

SEB

SE

(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

20

149

000

20

001

174

40

174

Em

plo

ymen

t19

93sq

uar

ed0

000

000

09

00E

076

00E

075

57E

041

92E

04C

anad

a6

824

100

570

121

30

571

108

117

956

9A

vera

geag

e4

125

502

50

044

002

61

628

471

3

wor

kfor

cew

ith

HS

dip

lom

a2

126

147

40

010

000

72

427

151

1

wor

kfor

cem

ale

045

11

290

000

60

007

207

61

15R

egio

nal

pri

cein

dex

348

110

324

540

283

11

692

527

4327

414

3U

nio

nize

dsh

opw

orke

rs29

640

675

340

046

034

654

479

625

12lo

g(m

achi

nes

pro

du

ctio

nw

orke

rs)

298

5432

069

025

90

155

183

427

698

Ind

exof

dir

ect

invo

lvem

ent

( z)

uni

on12

566

510

020

275

023

210

447

437

99In

dex

ofd

irec

tin

volv

emen

t( z

)no

nuni

on22

589

352

710

156

019

552

953

292

47

R2

054

777

54

corr

ectl

ycl

assi

ed

068

8Sa

mp

lesi

zen

128

332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant


layoffs or downsizing in the four years preceding the survey thanwere managers at low-involvement plants (results available onrequest)

6 Qualitative EvidenceA Quasinatural Experiment

As the above section showed EI is correlated with higher wagesWhat we would really like to know is If a random plant adopts EIwill wages rise Our OLS regression results do not necessarily shedlight on this question because the determinants of adoption of EI maybe correlated with the determinants of EIrsquos effect on wages (Athey andStern 1998)

By directly observing rms both before and after they introducedEI we can gain some insight into the causal processes that might linkthe two We learned during visits made for other reasons that theplant described below was about to change its EI policy In the courseof writing this paper we made arrangements to visit it again withoutknowing what the outcome of the policies had been (The semistruc-tured interviews we used for this portion of the research writtenbefore we made our second visit are available from the authors) Thisdesign reduces the sample-selection problems endemic to case-studyresearch

We wanted to nd a plant that changed its EI policies and thenlook at what happened to its wages In 1996 Helper visited Forest CityTechnologies in the course of a project on pollution prevention Thisrmrsquos four plants are located in the small town of Wellington Ohiowhich is about one hour south of Cleveland The rm was estab-lished in 1956 and has grown steadily since It remains nonunionIn 1996 the rm had 450 employees almost 13 more than it hadin 1992

A large part of this rmrsquos operation involved putting anticorro-sion and other types of coatings onto fasteners used in automotiveengines The business was highly competitive our interviewees com-plained of ldquoGestapo-like tacticsrdquo on the part of their customers todepress prices The rm made about 200 products which it suppliedto dozens of customers Although the rm had some contracts thatwent for ldquoyears and yearsrdquo much of the business is done on a short-term basis with no contracts if a customer sends a batch of partsto be coated there is no assurance if or when it will send anotherone

The core of Forest Cityrsquos process is getting many small odd-shaped parts as fasteners are wont to be into some kind of mold


mount or convenient position in which to apply a coating en masseForest Cityrsquos competitive advantage appeared to lie in having engi-neers who could design ever more clever jigs xtures and leads toorient the parts quickly and precisely so that the coating could beapplied by machine rather than by hand

All processes were intensive in the use of unskilled labor onaverage direct labor accounted for 55 of costs The operators werepaid on average $8 per hour (about average for the area) training formost jobs consisted of watching someone else do the process for awhile However the nature of work appeared to vary a fair amountacross processes

In one area the fasteners were painted with a thin strip of sealantcoating which had to go in a precise location The rmsrsquo engineershad gured out how to use tumblers (big bowls of parts that arejiggled by an electric motor underneath) to orient the small parts sothat the coating could be applied by machine rather than by handThe operatorsrsquo job was to watch the parts coming out of the machineto remove any that were defective and to call a skilled maintenanceworker if the machine stopped In practice the operator could notcheck every part because the parts came out very quickly (Also thework quickly induced boredom as could readily be observed on theoperatorsrsquo faces) If quality was very important (as in an airbag part)extra inspectors were added

In contrast in the rubber-coating area (known in the plant asldquoshake and bakerdquo) the operators paced themselves They placed smallrubber gaskets on trays with cavities (like a mufn pan with holes formany tiny mufns) and then switched on a vibrating mechanism toget the gaskets to fall into the holes After putting a few gaskets inholes by hand the operator put another tray on top and then placedthem in the oven to bake When they came out of the oven she visu-ally inspected them and packed them into shipping containers Thepace was not particularly onerous and relations with managementseemed quite friendly (As we stood there watching the managerwho was leading us around started to help the operator orient theparts completely without fanfaremdashhe seemed to feel that since hewas standing there he might as well help out This was the only caseof a manager performing such assistance we observed on our plantvisits)

Forest City had started a suggestion program in late 1992 but itseemed moribund in 1996 Operators were not expected to contributesuggestions for improvement and typically did not stay more thana year or two Particularly in the painted-sealant area there seemedto be unrealized potential for involving operators For example these


operators spent all day watching how the parts came out of the tum-blers and into the feeder trays if asked they might well have beenable to make suggestions about the circumstances under which mis-alignments or other defects are likely to occur There was no quality-circle program in either 1992 or 1996

The rmrsquos management agreed there was untapped potentialIn a 1993 survey the rmrsquos marketing director listed ldquoincreasingemployee involvementrdquo as ldquoextremely importantrdquo (Interestingly healso listed ldquomoderating the growth of wages and benetsrdquo as ldquoveryimportantrdquo) At the time of our visit in summer 1996 the rm wasin the early stages of implementing an employee empowerment pro-gram called the ldquo5-point starrdquo program developed by DimensionsInternational a Pittsburgh consulting rm The drive to do this wasspearheaded by the HR manager

In cooperation with the consulting rm Forest City identiedve functions of supervisors the plan was to devolve these amongwork teams of about ve people each When a worker took over anadditional function they earned wage incentives in addition therewas prot sharing and seniority bonuses To explain the system toemployees (and to demonstrate why their income would now uctu-ate with the business cycle) nancial information had just begun tobe posted in the lunchroom

We interviewed the human resource vice president again inJuly 1999 The base wage had risen slightly to $885 per hour Butthe pay-for-knowledge system had raised wages signicantly in twoways The rst was a direct outcome of the program workers couldget up to $1210 per hour for having all ve star points and doingwell on a subjective evaluation About 60 of workers had earnedat least one star point and the average wage had increased to $10per hour well above the rate of ination (Note that this increasewas not a foregone conclusion if the pay-for-knowledge systemexisted but workers did not get certied there would be no payincrease)

A second source of wage increase was a bonus system basedon rm performance The bonus had ranged from $1500 to $2000 peryear 7 to 10 of average straight-time earnings The HR managerfelt that the prot increase was due in substantial part to the EI sys-tem The evidence for this assertion was varied but indirect indi-vidual tasks done by operators acting as star points were cheaper(and higher quality) than when done by supervisors turnover wasdown particularly among those who had been at the plant 2ndash3 yearsemployment at the rm had continued to increase (to 515) and the


plant managers had changed from serious skepticism about the pro-gram to enthusiastic support of it

7 Discussion and Conclusion

After qualitative results from dozens of plant visits and quantitativeresults from a large survey what have we learned

The preponderance of evidence suggests that EI raises wagesfor blue-collar workers This evidence is broadly consistent withtheories of human capital efciency wages incentives and rentsharing We also found that managers usually implemented theseprograms in ways that were consistent with economistsrsquo notions ofincentives and complementarity because rms with more involve-ment tended also to have more employment security and contingentpay

We had less success uncovering the drivers of the wage-involvement relationship As discussed below we found the mostsupport for efciency-wage notions and no support for hypothesesbased on compensating differences

The ve theories we examined posited that EI affects wages byaffecting workersrsquo levels of skill utility and bargaining power

Skill We found qualitative support for human-capital hypothe-ses that involvement leads to higher wages because it requireshigher skill For example in our qualitative quasi-experimentmanagement believed that training was crucial for the successfuldelegation of supervisory tasks to workers We found modestquantitative support in that plants with more involvement havea higher percentage of high-school graduates in their workforcesHowever we found no correlation between EI and training in thedataCompensating differences In our eldwork we found that workersdid not consider some types of EI (such as participation in prob-lem solving) to be onerous and that introduction of the types ofinvolvement they did nd onerous (additional tasks to be doneon the line in the same amount of time) were not typically asso-ciated with increased compensation In our quantitative work wedid not nd that plants that introduced additional forms of com-pensation (such as protection from layoffs) paid lower wages Thuswe found almost no support for the compensating-differences ideathat involvement leads to higher wages because it requires moreeffort or more onerous effort than production work However the


frequent combination of EI with supportive HR policies is predictedby theories of complementarityBargaining power We found a number of ways in which EI increasedworkersrsquo access to rents Plants with more supervisors per workerhad both lower wages and less employee involvement This resultis consistent both with gift-exchange versions of efciency wageswhere workers feel management trusts them more if there are fewersupervisors and with monitoring-cost arguments where a com-bination of higher wages and employee self-policing is a substi-tute for supervisors In our eldwork workers frequently reportedthat their participation in EI programs was a form of gift exchangewith management This nding was echoed in our regressions inthat in plants with high involvement managers reported work-forces were more loyal However we did not nd statistical supportfor the hypothesis that this loyalty-mediated participation explainshigher wages We did nd evidence that paying a higher wagehelps managers to protect an investment in training by reducingturnover

In our eldwork we found a number of workplaces that seemedto be run according to management-by-stress principles whereintroduction of EI led to workersrsquo knowledge being used againstthem to speed up their work and to facilitate the startup of rivallower-wage plants But (perhaps not surprisingly in a survey lledout by managers) we did not nd direct support for the theory inour data

We did nd indirect support however for the idea common torent-sharing and management-by-stress theories that union partici-pation in EI programs increases their benets to workers Differentaspects of EI lead to wage increases in union and nonunion plantsIn union plants wages rise only if the plant has high representa-tive involvement the effect of direct involvement on wages wasnegative In nonunion plants workers did receive higher wagesif they had direct involvement but not enough to overcome theunion-nonunion pay gap This result supports one hypothesis of themanagement-by-stress viewmdashthat representative employee involve-ment is better for workers in the presence of unionsmdashbut not thestronger argument that workers benet from EI only if a union ispresent

While we found consistent evidence that EI leads to higher paywe found that it does not increase the probability of plant survivalOur evidence is consistent with the possibility that typical EI plansare poorly implemented even though best-practice plans can improve


organizational performance A story that is consistent with our evi-dence would be the following

Many rms adopted EI in the early 1990s as part of a fadone that had low costs but also low benets However some rmsadopted the programs quite intensively These plants saw signi-cant increases in xed costs These costs included expenditures ofmoney for training and of management time for responding to fearsabout what the program would mean participating in quality-circlemeetings responding to suggestions etc These upfront expenditurescannot be recouped if the program is canceled They were quitelarge for example the EI program reduced Forest Cityrsquos protabil-ity in its rst two years according to the HR vice president whochampioned the program In addition complementary HR policiessuch as no-layoff policies make labor a quasixed factor Even if EIon average eventually improves performance enough to outweighthese costs a liquidity-constrained rm can still go bankrupt ifdemand shrinks enough that the rm cannot pay its higher xedcosts

The auto-supplier industry is characterized by such severecompetitive conditions For example 40 of plants in the databasehad laid off at least 10 of their workforces in the 4 years prior tothe survey and these were all plants that had survived long enoughto be surveyed Thus higher death rates due to higher xed costswere quite possible even if productivity and quality were risingHowever if a rm managed to avoid bad product-market shockssubstituting the higher xed costs of EI for lower variable costs (dueto less scrap and more efcient material and labor usage due toworker suggestions) could be quite protable leading the plant toexpand13

Our results are still preliminary One set of problems concernsthe internal validity of the quantitative results Modest changes inspecication sometimes changed results (or more commonly theirstatistical signicance) For example results often depended onwhether we pooled subindices into a single index The causal connec-tion between adoption of new workplace practices and organizationalperformance is also in doubt However we do not nd much supportfor the theory that desperate plants adopt EI and then still go out ofbusiness

13 We attempted to test this hypothesis by investigating the effect of changes inthe sales of the car model (eg Chevrolet Cavalier) most important to each of ourplants However this variable had little effect on survival either by itself or inter-acted with EI A plausible explanation is that most supplier plants are quite diversiedacross car models so that loss of sales of one model did not reduce total demandvery much


An additional set of problems concerns the external validity ofthe quantitative results Most of our hypotheses received some qual-itative support Nevertheless few of the hypotheses received muchsupport from the quantitative data in part due to modest samplesizes large measurement error and low precision of estimates Manyof our measures look at the existence of programs rather than thethoroughness or consistency of their implementation increasing themeasurement error14 In future analysis we can control for theseeffects more thoroughly by separating out measures of the extent ofparticipation in programs (such as numbers of suggestions receivedand subjective assessments of program effectiveness)

In general we have more condence in results that are replicatedusing multiple methods One thing we have learned from our plantvisits is that EI programs are adopted in a dazzling variety of ways Infuture work we can link these insights more closely with the surveydata to better disentangle which types of programs have which typesof effects

References

Adler PS 1993 ldquoThe New lsquoLearning Bureaucracyrsquo New United Motors Manufactur-ing Incrdquo in B Staw and L Cummings eds Research in Organizational BehaviorGreenwich CT JAI Press 111ndash194

Akerlof G 1984 ldquoGift Exchange and Efciency Wage Theoryrdquo American EconomicReview 74 (2) 79ndash83

Arnold SJ 1979 ldquoA Test for Clustersrdquo Journal of Marketing Research 16 545ndash551Athey S and S Stern 1998 ldquoAn Empirical Framework for Testing Theories about

Complementarity in Organizational Designrdquo NBER Working Paper 6600Bearse PM H Bozdogan and AM Schlottman ldquoEmpirical Econometric Modeling of

Food Consumption Using a New Informational Complexity Approachrdquo Journal ofApplied Econometrics 12 (5) 563ndash592

14 One situation we encountered surprisingly often was workers feeling that man-agement had reneged on promises and withdrawing participation from the programOne would not expect such an EI program (one with little actual involvement) to bevery effective in improving rm performance enough to keep plants open A promi-nent example of perceived reneging occurred at a large steel mill where the UnitedSteelworkers at the rmrsquos Cleveland Works pulled out of all joint programs after man-agement in 1995 announced participation in a nonunion joint venture to make steel inAlabama Thousands of workers had lost their jobs at the facility over the preceding15 years and the union saw managementrsquos move as signaling a lack of commitmentldquoHow can we be like family here if theyrsquore setting up a competing plant somewhereelserdquo asked one union ofcial They also felt that some of the prots being used toinvest in the new plant had come from their hard work and should be used to com-pensate them for sacrices (eg wage cuts layoffs and increased work effort) in leantimes Managers did not see their behavior as reneging they regarded the Alabamaproject as ldquoanother business venturerdquo and did not see why the union should be con-cerned This rm announced its bankruptcy in December 2001 For other examples ofsuch incidents see MacDufe and Helper (1997 p 127) Helper (1998)


Becker G 1975 Human Capital Second edition Chicago University of Chicago PressBlack SE and LM Lynch 1997 ldquoHow to Compete The Impact of Workplace Practices

and Information Technology on Productivityrdquo Working Paper 6120 Cambridge MANational Bureau of Economic Research

Calinski RB and J Harabasz 1974 ldquoA Dendrite Method for Cluster Analysisrdquo Com-munications in Statistics 3 1ndash27

Dickens W 1986 ldquoWages Employment and the Threat of Collective Action by Work-ersrdquo NBER Working Paper 1856

Dow GK 1993 ldquoWhy Capital Hires LabormdashA Bargaining Perspectiverdquo American Eco-nomic Review 83 (1) 118ndash134

Drago R 1996 ldquoWorkplace Transformation and the Disposable Workplace EmployeeInvolvement in Australiardquo Industrial Relations 35 (4) 526ndash543

Eisenhardt K 1989 ldquoBuilding Theories from Case Study Researchrdquo Academy of Man-agement Review 14 (4) pp 532ndash550

Groshen E 1991 ldquoFive Reasons Why Wages Vary among Employersrdquo Industrial Rela-tions 30 (3) 350ndash381

Helper SR 1998 ldquoLean Production and the Specter of Mexicordquo in S Babson andH Juarez Nunez eds Confronting Change Auto Workers and Lean Production in NorthAmerica Detroit Wayne State University Press

Helper SR 1999 ldquoComplementarity and Cost Reduction Evidence from the AutoSupply Industryrdquo NBER Working Paper 6033 (revised)

Helper SR and R Parkin 1995 ldquoThe Effects of Down-sizing on Long-Term CorporatePerformancerdquo Report to the US Department of Labor Working Paper Departmentof Economics Case Western Reserve University

Henriques I and P Sadorsky 1999 ldquoThe Relationship between Environmental Com-mitment and Managerial Perceptions of Stakeholder Importancerdquo Academy of Man-agement Journal 42 (1) 87ndash99

Ichniowski C TA Kochan D Levine C Olson and G Strauss 1996 ldquoWhat Worksat Work Overview and Assessmentrdquo Industrial Relations 35 (3) 299ndash333

Ichniowski C G Prennushi and K Shaw 1997 ldquoThe Effects of Human ResourceManagement Practices on Productivityrdquo American Economic Review 87 (3) 291ndash313

Katz L 1987 ldquoEfciency Wage Theories A Partial Evaluationrdquo in S Fischer ed NBERMacroeconomics Annual Cambridge MA The MIT Press

Kochan T H Katz and N Mower 1984 Worker Participation and American UnionsKalamazoo MI Upjohn Institute Press

Lawler III EE SA Mohrman and GE Ledford Jr 1995 Creating High PerformanceOrganizations Practices and Results of Employee Involvement and Total Quality Manage-ment in Fortune 1000 Companies San Francisco JosseyBass

Lazear E 1979 ldquoWhy Is There Mandatory Retirementrdquo Journal of Political Economy 871261ndash1284

Ledford GE 1991 ldquoThree Case Studies on Skill-Based Payrdquo Compensation and BenetsReview 23 (2) 11ndash23

Levine DI 1993 ldquoWhat Do Wages Buyrdquo Administrative Science Quarterly 38 (3)462ndash483

Levine DI 1995 Reinventing the Workplace Washington BrookingsLevine DI and R Parkin 1996 ldquoWork Organization Employment Security and

Macroeconomic Stabilityrdquo Journal of Economic Behavior and Organization 24 (3)251ndash271

Levine DI and LD Tyson 1990 ldquoParticipation Productivity and the Firmrsquos Environ-mentrdquo with in Paying for Productivity Alan Blinder ed Brookings Institution pp183ndash244


Lindbeck A and D Snower 1986 ldquoWage Setting Unemployment and Insider-OutsiderRelationsrdquo American Economic Review 76 (2) 235ndash239

MacDufe JP and SR Helper 1999 ldquoCreating Lean Suppliers Diffusing Lean Produc-tion throughout the Supply Chainrdquo in P Adler M Fruin and J Liker eds Remadein America Transforming and Transplanting Japanese Management Systems New YorkOxford University Press

Milgrom P and J Roberts 1992 Economics Organization and Management EnglewoodCliffs NJ Prentice Hall

Milgrom P and J Roberts 1995 ldquoComplementarities and Fit Strategy Structure andOrganizational Change in Manufacturingrdquo Journal of Accounting amp Economics 19(2amp3) 179ndash208

Milgrom P YY Qian and J Roberts 1991 ldquoComplementarities Momentum and theEvolution of Modern Manufacturingrdquo American Economic Review 81 (2) 84ndash88

Milligan GW 1980 ldquoAn Examination of the Effect of Six Types of Error Perturbationof Fifteen Clustering Algorithmsrdquo Psychometrika 45 325ndash342

Milligan GW and MC Cooper 1985 ldquoAn Examination of Procedures for Determiningthe Number of Clusters in a Data Setrdquo Psychometrika 50 (2) 159ndash179

Osterman P 1995 ldquoSkill Training and Work Organization in American Establish-mentsrdquo Industrial Relations 34 125ndash146

Parker M 1985 Inside the Circle A Union Guide to QWL Boston South End PressParker M and J Slaughter 1988 Choosing Sides Unions and the Team Concept Detroit

Labor Notes BookPfeffer J 1994 Competitive Advantage through People Boston Harvard Business School

PressSarasvathy DK HA Simon and L Lave 1998 ldquoPerceiving and Managing Business

Risks Differences between Entrepreneurs and Bankersrdquo Journal of Economic Behavioramp Organization 33 (2) 207ndash225

Sheahan M Libby Manion and Susan Helper 1996 A Union Guide to QS9000 DetroitLabor-Management Council for Economic Renewal

Womack J D Jones and D Roos 1990 The Machine that Changed the World New YorkRawson Associates

Wruck K and M Jensen 1994 ldquoScience Specic Knowledge and Total Quality Man-agementrdquo Journal of Accounting and Economics 18 247ndash287


(Ichniowski et al 1996 Black and Lynch 1997) By employee involve-ment we mean blue-collar workers using abstract reasoning skillsas well as performing manual tasks Activities using these skillsinclude meeting with a group to devise solutions to quality or safetyproblems providing suggestions for improvement and setting upmachines to make different parts These activities can occur in formalinstitutions (such as a suggestion system or pay-for-knowledge plan)or informally (See Table I)

While there is a growing consensus that EI can improve out-comes for rms few studies have examined the question of whethernew work practices can improve outcomes for employees Some authorsclaim EI helps workers by providing them with higher skills pay andquality of work life (Womack et al 1990) However others fear that EIleads to speedups lower wages less safety and more job loss (Drago1996 Parker and Slaughter 1988)

Our study examines data from US and Canadian auto suppliersto investigate whether EI has measurable effects on employeesrsquo payand on the survival of the plants in which they work In addition toquantitative analysis we report on a number of visits to plants withvery different levels of EI

In the Section 2 below we briey discuss hypotheses about theeffects of EI on compensation derived from ve standard theories fromlabor economics To aid intuition we provide examples from our eldresearch Our eld research also led us to generate several hypothe-ses that we did not derive from standard economic theories Theseare presented in Section 3 Section 4 describes the methods used totest the hypotheses survey data and case studies Section 5 providesquantitative results Section 6 describes some qualitative evidence andSection 7 concludes

2 Theories Relating Workplace Practices andEmployeesrsquo Outcomes

21 Summary of Theories

We rst summarize ve theories of why workplaces with EI mighthave different outcomes for employees than workplaces without EIhuman capital compensating differences efciency wages incentivesand complementarity and rent sharing (Groshen 1991) We also lookat a type of rent-sharing theory called management by stress For eachtheory we draw on our eld research to give examples of how EIoperates in practice Section 3 elaborates hypotheses about the factorsdriving the relationship between wages and EI




Control Variable




000 038 100


000 030 100


000 000 100


000 000 100


000 017 100


000 014 100


000 070 100


000 042 100


000 008 100


000 093 100

(continued)


TABLE I (continued)



000 008 100


000 027 100


000 006 100


000 009 100


000 003 100

Outcome Variables




470 400 137



018 017 070


mdash 013 mdash








































22 Basic Hypotheses













and










and




and

























4 Data and Methods
















































TA

BL

EII

B

as

el

ine

Wa

ge

Eq

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line

con

trol

sfr

omTa

ble

IID

iffe

ren

ces

inu

nion

vs

non

uni

onco

ef

cien

tsw

ere

not

sign

ica

nt


TA

BL

EV

IT

he

Eff

ects

of

Cl

us

ter

so

nW

ag

es

a

log(

Un

skil

led

Wag

es)

log(

Uns

kill

edC

omp

ensa

tion

)R

elat

ive

Com

pen

sati

onL

abor

Turn

over

Rat

e

BSE

BSE

BSE

BSE

(Con

stan

t)0

861

018

90

942

022

61

235

122

226

387

947

0A

vera

geag

e0

010

000

20

013

000

20

036

001

50

204

008

6C

anad

a0

352

003

90

286

004

20

062

026

53

344

201

3

wor

kfor

cew

ith

HS

dip

lom

a0

019

000

10

002

000

10

006

000

40

074

003

5lo

g(em

plo

ymen

t)0

060

001

40

075

001

70

271

008

80

828

059

2lo

g(m

achi

nes

pro

dn

wor

kers

)0

005

001

30

003

001

50

052

007

31

118

063

0

wor

kfor

cem

ale

000

40

000

000

40

001

000

90

003

001

30

021

Reg

iona

lp

rice

ind

ex0

184

014

10

182

016

50

405

088

92

465

625

1N

onu

nion

hig

h-in

volv

emen

tcl

ust

er0

090

003

20

105

003

60

306

021

00

325

123

2U

nio

nlo

w-i

nvo

lvem

ent

clu

ster

015

20

038

017

70

045

026

50

254

277

71

265

Un

ion

hig

h-in

volv

emen

tcl

ust

er0

196

003

50

242

004

00

642

023

60

852

137

8

R2

051

10

467

015

10

095

F-t

est

ofth

eeq

ual

ity

ofbo

ld10

910

120

642

466

097

2co

ef

cien

tsSa

mp

lesi

zen

319

319

305

309

Sign

ica

nt

at10

Si

gni

cant

at5

Not

eC

lust

ers

are

de

ned

inTa

ble

III(

B)

Bas

elin

e(o

mit

ted

)cl

ust

eris

non

uni

onan

dlo

win

volv

emen

ta

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant











BL

EV

II

Tes

ts

of

Th

eo

rie

s

(A)

Doe

sTr

ain

ing

Exp

lain

the

Wag

e-In

volv

emen

tR

elat

ion

a

All

Pla

nts

(1)

(2)

(3)

(4)

Dir

ect

EIb

ln(U

nski

lled

Wag

es)

ln(U

nsk

illed

Wag

es)

ln(U

nski

lled

Wag

es)

Sam

ple

BSE

BSE

BSE

BSE

Un

ion

008

90

122

012

40

027

011

80

024

012

20

024

Ind

exof

trai

ning

( z)

non

un

ion

008

40

087

001

30

019

000

80

018

Ind

exof

trai

ning

( z)

un

ion

008

10

068

001

50

010

001

40

010

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

005

00

013

004

90

012

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

90

020

001

70

019

R2

012

00

516

049

80

517

F-t

est

ofth

eeq

ual

ity

ofbo

ldco

ef

cien

ts1

157

656

80

719

353

3Sa

mp

lesi

zen

332

319

319

319

Pla

nts

wit

hou

tR

ecen

tH

irin

g

(5)

(6)

(7)

ln(U

nsk

ille

dW

age)

ln(U

nski

lled

Wag

e)ln

(Un

skil

led

Wag

e)

Sam

ple

BSE

BSE

BSE

Un

ion

009

90

138

004

50

026

007

60

027

Ind

exof

trai

ning

( z)

non

un

ion

000

30

021

004

00

019

Ind

exof

trai

ning

( z)

un

ion

005

80

009

005

40

009

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

006

60

024

007

10

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

006

50

012

005

10

021

R2

061

30

586

062

2F

-tes

tof

the

equ

alit

yof

bold

coef

ci

ents

136

90

312

081

3Sa

mp

lesi

zen

8686

86


(B)

Ef

cien

cyW

ages

and

Sup

ervi

sion

Turn

over

Turn

over

(1)

(2)

Who

leSa

mp

leB

SEB

SE

Un

ion

044

61

308

088

61

350

z(Tr

ain)

087

90

532

079

20

545

z(D

irec

t)0

019

060

9ln

(uns

kill

edw

age)

123

602

860

R2

010

10

171

Sam

ple

size

n29

529

5

Th

eE

ffec

tof

Sup

ervi

sion

Doe

sL

oyal

tyM

edia

te

Dir

ect

EIc

ln(U

nsk

ille

dW

ages

)D

irec

tE

Icln

(Un

skil

led

Wag

es)

Who

leSa

mp

leB

SEB

SEB

SEB

SE

Un

ion

006

90

120

012

40

025

005

50

116

012

50

026

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

60

013

005

20

021

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

50

018

001

70

014

Ind

exof

mgt

re

por

tsof

wor

ker

loya

lty

( z)

nonu

nio

n0

420

006

60

009

002

4In

dex

ofm

gt

rep

orts

ofw

orke

rlo

yalt

y( z

)u

nion

034

60

100

000

10

017

Sup

ervi

sors

w

orke

r1

168

037

20

211

011

3

F-t

est

onbo

thlo

yalt

yin

dic

es5

983

461

727

633

048

4(a

ndsu

per

viso

rsh

opw

orke

r)

R2

013

20

525

024

50

516

Sam

ple

size

n33

031

733

231

9

(con

tinu

ed)


TA

BL

EV

II

(co

ntin

ue

d)

(C)

Wag

esan

dIn

tens

ive

Em

plo

yee

Invo

lvem

enta

Dep

end

ent

Var

iabl

elo

g(U

nsk

ille

dW

age)

Ind

epen

den

tV

aria

ble

BSE

Un

ioni

zed

shop

wor

kers

009

940

031

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

000

870

018

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

003

90

019

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

80

016

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

70

022

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

001

40

018

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

40

020

Du

mm

yfo

rin

tens

ive

EI

and

non

uni

on0

038

004

8D

um

my

for

inte

nsiv

eE

Ian

du

nio

n0

033

006

1

R2

053

6F

-sta

tist

icon

incl

usi

onof

du

mm

yva

riab

les

046

1Sa

mp

lesi

zen

319

Sign

ica

ntat

10

Si

gni

cant

at5

a

All

regr

essi

ons

cont

roll

edfo

rw

orkf

orce

aver

age

age

ad

um

my

for

Can

ada

the

per

cent

age

ofth

ew

orkf

orce

wit

ha

hig

h-sc

hoo

ld

iplo

ma

log(

emp

loym

ent)

lo

g(m

ach

ines

p

rod

uct

ion

wor

kers

)th

ep

erce

ntag

eof

the

wor

kfor

cem

ale

and

the

regi

onal

pri

cein

dex

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant

b

Th

eco

ef

cien

tson

dir

ect

EI

do

not

chan

geby

ast

atis

tica

llysi

gni

can

tam

oun

tw

hen

the

ind

ices

oftr

aini

ngar

ead

ded

c

Th

eco

ef

cien

tson

dir

ect

emp

loye

ein

volv

emen

td

on

otch

ange

bya

stat

isti

cally

sign

ica

ntam

ount

wh

enth

ein

dic

esgi

ftsu

per

visi

onor

oflo

yalt

yar

ead

ded

[com

par

ing

colu

mn

(2)

or(4

)he

rean

dth

eco

rres

pon

din

gre

gres

sion

inTa

ble

V]


























TA

BL

EV

III

Eff

ec

tso

fE

mpl

oy

ee

Inv

ol

ve

me

nt

on

Em

plo

ym

en

tG

ro

wth

1

99

3ndash1

99

9

(1)

(2)

(3)

OL

SE

mpl

oym

ent

1999

OL

SE

mp

loym

ent

1999

Log

itS

urv

ival

Sam

ple

Fil

ms

Stil

lin

Bu

sine

ssFu

llSa

mp

leFu

llSa

mp

lein

2000

BSE

DP

DX

SEB

SE

(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

20

149

000

20

001

174

40

174

Em

plo

ymen

t19

93sq

uar

ed0

000

000

09

00E

076

00E

075

57E

041

92E

04C

anad

a6

824

100

570

121

30

571

108

117

956

9A

vera

geag

e4

125

502

50

044

002

61

628

471

3

wor

kfor

cew

ith

HS

dip

lom

a2

126

147

40

010

000

72

427

151

1

wor

kfor

cem

ale

045

11

290

000

60

007

207

61

15R

egio

nal

pri

cein

dex

348

110

324

540

283

11

692

527

4327

414

3U

nio

nize

dsh

opw

orke

rs29

640

675

340

046

034

654

479

625

12lo

g(m

achi

nes

pro

du

ctio

nw

orke

rs)

298

5432

069

025

90

155

183

427

698

Ind

exof

dir

ect

invo

lvem

ent

( z)

uni

on12

566

510

020

275

023

210

447

437

99In

dex

ofd

irec

tin

volv

emen

t( z

)no

nuni

on22

589

352

710

156

019

552

953

292

47

R2

054

777

54

corr

ectl

ycl

assi

ed

068

8Sa

mp

lesi

zen

128

332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant











































References


















































Control Variable




000 038 100


000 030 100


000 000 100


000 000 100


000 017 100


000 014 100


000 070 100


000 042 100


000 008 100


000 093 100

(continued)


TABLE I (continued)



000 008 100


000 027 100


000 006 100


000 009 100


000 003 100

Outcome Variables




470 400 137



018 017 070


mdash 013 mdash








































22 Basic Hypotheses













and










and




and

























4 Data and Methods
















































TA

BL

EII

B

as

el

ine

Wa

ge

Eq

uatio

ns

(1)

(2)

(3)

(4)

ln(u

nsk

illed

wag

es)

ln(u

nski

lled

com

pen

sati

on)

Rel

ativ

eC

omp

ensa

tion

Lab

orTu

rnov

erR

ate

BSE

BSE

BSE

BSE

(Con

stan

t)0

859

018

20

968

021

21

266

121

327

957

806

6A

vera

geag

e0

010

000

20

012

000

20

034

001

30

204

008

4C

anad

a0

361

003

90

295

004

50

039

025

23

819

166

0

wor

kfor

cew

ith

HS

dip

lom

a0

002

000

10

002

000

10

006

000

40

070

002

5lo

g(em

plo

ymen

t)0

069

001

30

085

001

50

314

008

40

861

056

3lo

g(m

achi

nes

pro

dn

wor

kers

)0

007

001

20

004

001

40

063

007

81

240

052

3

wor

kfor

cem

ale

000

40

000

000

40

001

000

80

003

001

40

022

Reg

iona

lp

rice

ind

ex0

216

013

20

198

015

30

395

089

04

392

585

4U

nio

nize

dsh

opw

orke

rs0

116

002

60

142

003

00

281

017

11

363

113

9

R2

049

70

449

013

90

100

Sam

ple

size

n31

931

930

530

9

Sign

ica

nt

at10

Sign

ica

ntat

5



51 Incidence








TA

BL

EII

IIn

cid

en

ce

of

Dir

ec

tE

mpl

oy

ee

Inv

ol

ve

me

nt

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

(Con

stan

t)0

835

085

80

872

086

80

850

085

40

547

084

90

429

081

7A

vera

geag

e0

026

001

00

024

000

90

025

000

90

030

000

90

027

000

9C

anad

a0

416

016

20

404

017

50

436

017

30

395

017

10

330

016

4

wor

kfor

cew

ith

HS

dip

lom

a0

006

000

30

006

000

30

005

000

30

005

000

30

004

000

3lo

g(em

plo

ymen

t)0

257

005

40

256

006

00

251

005

90

238

005

90

207

005

7

log(

mac

hine

sp

rod

nw

orke

rs)

001

30

051

001

40

056

000

00

056

000

80

055

001

00

053

w

orkf

orce

mal

e0

001

000

20

002

000

20

002

000

20

003

000

20

003

000

2R

egio

nal

pri

cein

dex

003

80

673

001

20

632

009

40

622

011

40

616

024

00

592

Un

ioni

zed

shop

wor

kers

007

20

120

008

90

121

004

70

119

029

90

128

040

30

126

Ind

exof

trai

ning

( z)

008

20

054

(con

tinu

ed)


TA

BL

EII

I(c

on

tin

ue

d)

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

Ind

exof

emp

loym

ent

secu

rity

( z)

uni

on0

195

008

5P

revi

ous

non

un

ion

021

90

067

Supp

orti

veH

Rp

ract

ices

( z)

uni

on0

170

008

20

175

007

8P

revi

ous

non

un

ion

033

50

103

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

031

70

081

032

10

069

Pre

viou

sn

onu

nio

n0

131

007

3

R2

011

40

120

015

60

168

023

7F

-tes

tof

the

equ

alit

yof

bold

coef

ci

ents

805

103

7014

566

Sam

ple

size

n33

233

233

233

233

2


(B)

Clu

ster

An

alys

isof

the

Pla

nts

Uni

on

Uni

on

Non

un

ion

N

onu

nio

n

AN

OV

AH

igh

Invo

lve

Low

Invo

lve

Hig

hIn

volv

eL

owIn

volv

eSi

ga

Var

iabl

esU

sed

toC

reat

eth

eC

lust

ers

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

III

III

Ind

exof

com

mit

tee

037

070

062

008

070

104

010

000

096

027

000

089

00

020

164

exis

ten

ce( z

)In

dex

ofd

irec

t0

600

530

490

960

790

720

670

650

521

050

920

680

120

1E20

invo

lvem

ent

( z)

Ind

exof

job

secu

rity

( z)

003

078

092

027

093

100

022

078

101

012

093

102

007

004

001

Ind

exof

grie

van

cep

lans

( z)

092

124

060

087

124

061

045

108

083

077

108

057

00

570

001

Sup

por

tive

HR

pra

ctic

es( z

)0

460

421

080

110

040

830

050

040

940

500

630

860

002

4E06

Ind

exof

imp

rove

men

t0

220

380

670

250

381

230

340

380

330

660

381

430

830

2E16

pro

cess

( z)

Em

plo

yee

invo

lvem

ent

( z)

092

091

080

034

034

073

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ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

50

018

001

70

014

Ind

exof

mgt

re

por

tsof

wor

ker

loya

lty

( z)

nonu

nio

n0

420

006

60

009

002

4In

dex

ofm

gt

rep

orts

ofw

orke

rlo

yalt

y( z

)u

nion

034

60

100

000

10

017

Sup

ervi

sors

w

orke

r1

168

037

20

211

011

3

F-t

est

onbo

thlo

yalt

yin

dic

es5

983

461

727

633

048

4(a

ndsu

per

viso

rsh

opw

orke

r)

R2

013

20

525

024

50

516

Sam

ple

size

n33

031

733

231

9

(con

tinu

ed)


TA

BL

EV

II

(co

ntin

ue

d)

(C)

Wag

esan

dIn

tens

ive

Em

plo

yee

Invo

lvem

enta

Dep

end

ent

Var

iabl

elo

g(U

nsk

ille

dW

age)

Ind

epen

den

tV

aria

ble

BSE

Un

ioni

zed

shop

wor

kers

009

940

031

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

000

870

018

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

003

90

019

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

80

016

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

70

022

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

001

40

018

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

40

020

Du

mm

yfo

rin

tens

ive

EI

and

non

uni

on0

038

004

8D

um

my

for

inte

nsiv

eE

Ian

du

nio

n0

033

006

1

R2

053

6F

-sta

tist

icon

incl

usi

onof

du

mm

yva

riab

les

046

1Sa

mp

lesi

zen

319

Sign

ica

ntat

10

Si

gni

cant

at5

a

All

regr

essi

ons

cont

roll

edfo

rw

orkf

orce

aver

age

age

ad

um

my

for

Can

ada

the

per

cent

age

ofth

ew

orkf

orce

wit

ha

hig

h-sc

hoo

ld

iplo

ma

log(

emp

loym

ent)

lo

g(m

ach

ines

p

rod

uct

ion

wor

kers

)th

ep

erce

ntag

eof

the

wor

kfor

cem

ale

and

the

regi

onal

pri

cein

dex

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant

b

Th

eco

ef

cien

tson

dir

ect

EI

do

not

chan

geby

ast

atis

tica

llysi

gni

can

tam

oun

tw

hen

the

ind

ices

oftr

aini

ngar

ead

ded

c

Th

eco

ef

cien

tson

dir

ect

emp

loye

ein

volv

emen

td

on

otch

ange

bya

stat

isti

cally

sign

ica

ntam

ount

wh

enth

ein

dic

esgi

ftsu

per

visi

onor

oflo

yalt

yar

ead

ded

[com

par

ing

colu

mn

(2)

or(4

)he

rean

dth

eco

rres

pon

din

gre

gres

sion

inTa

ble

V]


























TA

BL

EV

III

Eff

ec

tso

fE

mpl

oy

ee

Inv

ol

ve

me

nt

on

Em

plo

ym

en

tG

ro

wth

1

99

3ndash1

99

9

(1)

(2)

(3)

OL

SE

mpl

oym

ent

1999

OL

SE

mp

loym

ent

1999

Log

itS

urv

ival

Sam

ple

Fil

ms

Stil

lin

Bu

sine

ssFu

llSa

mp

leFu

llSa

mp

lein

2000

BSE

DP

DX

SEB

SE

(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

20

149

000

20

001

174

40

174

Em

plo

ymen

t19

93sq

uar

ed0

000

000

09

00E

076

00E

075

57E

041

92E

04C

anad

a6

824

100

570

121

30

571

108

117

956

9A

vera

geag

e4

125

502

50

044

002

61

628

471

3

wor

kfor

cew

ith

HS

dip

lom

a2

126

147

40

010

000

72

427

151

1

wor

kfor

cem

ale

045

11

290

000

60

007

207

61

15R

egio

nal

pri

cein

dex

348

110

324

540

283

11

692

527

4327

414

3U

nio

nize

dsh

opw

orke

rs29

640

675

340

046

034

654

479

625

12lo

g(m

achi

nes

pro

du

ctio

nw

orke

rs)

298

5432

069

025

90

155

183

427

698

Ind

exof

dir

ect

invo

lvem

ent

( z)

uni

on12

566

510

020

275

023

210

447

437

99In

dex

ofd

irec

tin

volv

emen

t( z

)no

nuni

on22

589

352

710

156

019

552

953

292

47

R2

054

777

54

corr

ectl

ycl

assi

ed

068

8Sa

mp

lesi

zen

128

332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant











































References
















































TABLE I (continued)



000 008 100


000 027 100


000 006 100


000 009 100


000 003 100

Outcome Variables




470 400 137



018 017 070


mdash 013 mdash








































22 Basic Hypotheses













and










and




and

























4 Data and Methods
















































TA

BL

EII

B

as

el

ine

Wa

ge

Eq

uatio

ns

(1)

(2)

(3)

(4)

ln(u

nsk

illed

wag

es)

ln(u

nski

lled

com

pen

sati

on)

Rel

ativ

eC

omp

ensa

tion

Lab

orTu

rnov

erR

ate

BSE

BSE

BSE

BSE

(Con

stan

t)0

859

018

20

968

021

21

266

121

327

957

806

6A

vera

geag

e0

010

000

20

012

000

20

034

001

30

204

008

4C

anad

a0

361

003

90

295

004

50

039

025

23

819

166

0

wor

kfor

cew

ith

HS

dip

lom

a0

002

000

10

002

000

10

006

000

40

070

002

5lo

g(em

plo

ymen

t)0

069

001

30

085

001

50

314

008

40

861

056

3lo

g(m

achi

nes

pro

dn

wor

kers

)0

007

001

20

004

001

40

063

007

81

240

052

3

wor

kfor

cem

ale

000

40

000

000

40

001

000

80

003

001

40

022

Reg

iona

lp

rice

ind

ex0

216

013

20

198

015

30

395

089

04

392

585

4U

nio

nize

dsh

opw

orke

rs0

116

002

60

142

003

00

281

017

11

363

113

9

R2

049

70

449

013

90

100

Sam

ple

size

n31

931

930

530

9

Sign

ica

nt

at10

Sign

ica

ntat

5



51 Incidence








TA

BL

EII

IIn

cid

en

ce

of

Dir

ec

tE

mpl

oy

ee

Inv

ol

ve

me

nt

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

(Con

stan

t)0

835

085

80

872

086

80

850

085

40

547

084

90

429

081

7A

vera

geag

e0

026

001

00

024

000

90

025

000

90

030

000

90

027

000

9C

anad

a0

416

016

20

404

017

50

436

017

30

395

017

10

330

016

4

wor

kfor

cew

ith

HS

dip

lom

a0

006

000

30

006

000

30

005

000

30

005

000

30

004

000

3lo

g(em

plo

ymen

t)0

257

005

40

256

006

00

251

005

90

238

005

90

207

005

7

log(

mac

hine

sp

rod

nw

orke

rs)

001

30

051

001

40

056

000

00

056

000

80

055

001

00

053

w

orkf

orce

mal

e0

001

000

20

002

000

20

002

000

20

003

000

20

003

000

2R

egio

nal

pri

cein

dex

003

80

673

001

20

632

009

40

622

011

40

616

024

00

592

Un

ioni

zed

shop

wor

kers

007

20

120

008

90

121

004

70

119

029

90

128

040

30

126

Ind

exof

trai

ning

( z)

008

20

054

(con

tinu

ed)


TA

BL

EII

I(c

on

tin

ue

d)

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

Ind

exof

emp

loym

ent

secu

rity

( z)

uni

on0

195

008

5P

revi

ous

non

un

ion

021

90

067

Supp

orti

veH

Rp

ract

ices

( z)

uni

on0

170

008

20

175

007

8P

revi

ous

non

un

ion

033

50

103

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

031

70

081

032

10

069

Pre

viou

sn

onu

nio

n0

131

007

3

R2

011

40

120

015

60

168

023

7F

-tes

tof

the

equ

alit

yof

bold

coef

ci

ents

805

103

7014

566

Sam

ple

size

n33

233

233

233

233

2


(B)

Clu

ster

An

alys

isof

the

Pla

nts

Uni

on

Uni

on

Non

un

ion

N

onu

nio

n

AN

OV

AH

igh

Invo

lve

Low

Invo

lve

Hig

hIn

volv

eL

owIn

volv

eSi

ga

Var

iabl

esU

sed

toC

reat

eth

eC

lust

ers

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

III

III

Ind

exof

com

mit

tee

037

070

062

008

070

104

010

000

096

027

000

089

00

020

164

exis

ten

ce( z

)In

dex

ofd

irec

t0

600

530

490

960

790

720

670

650

521

050

920

680

120

1E20

invo

lvem

ent

( z)

Ind

exof

job

secu

rity

( z)

003

078

092

027

093

100

022

078

101

012

093

102

007

004

001

Ind

exof

grie

van

cep

lans

( z)

092

124

060

087

124

061

045

108

083

077

108

057

00

570

001

Sup

por

tive

HR

pra

ctic

es( z

)0

460

421

080

110

040

830

050

040

940

500

630

860

002

4E06

Ind

exof

imp

rove

men

t0

220

380

670

250

381

230

340

380

330

660

381

430

830

2E16

pro

cess

( z)

Em

plo

yee

invo

lvem

ent

( z)

092

091

080

034

034

073

019

019

074

103

095

070

00

1E20

Ind

exof

wor

ker

grou

p0

710

900

740

690

890

800

480

300

700

870

890

710

160

1E20

in

uen

ceov

erp

olic

ies

( z)

Ind

exof

com

mit

tee

ampu

nio

n1

030

821

160

200

010

890

320

830

680

560

830

480

00

003

in

uen

ceov

er6

pol

icie

s( z

)In

dex

ofim

pro

vem

ent

033

017

073

082

097

095

044

074

072

048

040

104

00

1E15

thro

ugh

list

enin

g( z

)

Ind

exof

cont

inge

ntp

ay( z

)0

230

270

890

261

020

900

340

271

070

070

270

910

083

000

2In

dex

ofre

pre

sent

ativ

e0

900

920

910

180

180

940

270

080

860

530

540

760

00

014

invo

lvem

ent

( z)

(con

tinu

ed)


TA

BL

EII

I(c

on

tin

ue

d)

Un

ion

U

nion

N

onu

nion

N

onu

nion

A

NO

VA

Hig

hIn

volv

eL

owIn

volv

eH

igh

Invo

lve

Low

Invo

lve

Sig

a

Var

iabl

esU

sed

toC

reat

eth

eC

lust

ers

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

III

III

Ind

exof

task

sp

erfo

rmed

041

034

069

059

038

084

040

034

086

067

066

091

061

05E

19by

sem

iski

lled

wor

kers

( z)

Ind

exof

use

ofte

ams

( z)

010

000

070

041

000

109

033

044

067

036

000

113

002

02E

09In

dex

oftr

aini

ng( z

)0

180

001

350

120

040

610

010

060

610

050

220

890

40

070

709

Oth

erV

aria

bles

Ave

rage

age

383

339

00

636

388

638

50

652

331

833

00

570

352

835

00

585

00

610

008

Can

ada

019

000

040

024

000

043

009

000

029

008

000

028

00

430

821

w

orkf

orce

wit

hH

S78

69

850

020

03

701

480

00

260

881

11

900

023

61

796

580

00

196

70

020

020

631

dip

lom

alo

g(em

plo

ymen

t)5

605

530

835

385

551

025

305

330

964

864

871

140

013

000

1lo

g(m

achi

nes

pro

du

ctio

n1

891

831

132

021

771

071

811

701

061

481

440

900

020

460

02w

orke

rs)

w

orkf

orce

mal

e62

87

650

024

36

628

265

00

267

355

64

550

022

23

604

660

00

232

50

030

990

108

Reg

iona

lp

rice

ind

ex1

051

070

081

061

090

081

041

010

091

041

000

110

010

250

963

log(

com

pen

sati

onof

296

294

027

289

287

036

282

284

022

275

278

026

00

180

027

skil

led

)


log(

wag

esof

skill

ed)

265

264

024

260

264

035

255

256

021

250

248

024

00

30

049

log(

com

pen

sati

onof

un

skill

ed)

271

269

031

265

263

036

246

243

025

239

240

027

00

280

07

log(

wag

esof

uns

kille

d)

240

238

028

236

233

034

219

216

024

214

214

024

00

40

099

Rel

ativ

eco

mp

ensa

tion

507

500

151

476

450

123

463

400

131

439

400

139

00

170

175

Lab

ortu

rnov

erra

te5

212

008

623

822

005

237

364

0010

19

744

500

806

00

260

948

log(

tota

lno

em

plo

yees

1999

0

070

110

540

050

110

700

210

190

620

350

240

910

010

870

191

tota

lno

em

plo

yees

1992

)b

log(

tota

lno

em

plo

yees

1999

0

040

030

130

25to

tal

no

emp

loye

es19

92)c

Ou

tof

busi

ness

by19

990

170

000

380

180

000

390

160

000

370

090

000

290

210

920

13

Nu

mbe

rof

pla

nts

incl

ust

er98

7817

395

Sign

ica

nt

at10

Sign

ica

ntat

5

aI

Un

ion

vs

nonu

nio

n

rms

II

Low

-vs

hig

h-in

volv

emen

tu

nio

ncl

ust

ers

III

Low

-vs

hig

h-in

volv

emen

tno

nuni

oncl

ust

ers

bC

ond

itio

nal

onbe

ing

stil

lin

busi

nes

sin

1999

c

Em

plo

ymen

teq

ual

sn

egat

ive

in

nit

yfo

r

rms

that

shu

td

own

thu

son

lyth

em

edia

nis

wel

ld

en

ed


TA

BL

EIV

T

he

Ef

fe

ct

of

th

eS

um

ma

ry

Ind

ex

on

Wa

ge

sa

nd

Tu

rn

ov

er

a

(1)

(2)

(3)

(4)

ln(U

nski

lled

Wag

es)

ln(U

nski

lled

Com

pen

sati

on)

Rel

ativ

eC

ompe

nsa

tion

Turn

over

Rat

e

Var

iabl

eB

SEB

SEB

SEB

SE

(Con

stan

t)0

964

018

01

065

022

61

711

120

625

910

967

1A

vera

geag

e0

001

000

20

012

000

20

033

001

50

213

008

8C

anad

a0

345

003

80

279

004

50

122

025

83

158

206

6

wor

kfor

cew

ith

HS

dip

lom

a0

002

000

10

002

000

10

004

000

40

068

003

5lo

g(em

plo

ymen

t)0

059

001

30

075

001

60

253

008

80

692

059

5lo

g(m

achi

nes

pro

dn

wor

kers

)0

008

001

20

007

001

40

068

007

31

092

062

4

wor

kfor

cem

ale

000

30

000

000

40

001

000

80

003

001

20

021

Reg

iona

lp

rice

ind

ex0

208

013

00

207

016

80

275

088

33

145

625

8U

nio

nize

dsh

opw

orke

rs0

093

002

60

114

003

00

134

016

91

155

109

2E

mp

loye

ein

volv

emen

t( z

)no

nun

ion

003

60

015

003

30

015

018

90

111

051

40

607

Em

plo

yee

invo

lvem

ent

( z)

uni

on0

052

001

80

069

001

80

287

012

00

335

059

7

R2

051

60

473

016

30

093

F-t

est

ofE

Iin

dex

and

its

inte

ract

ion

wit

hu

nio

n6

807

234

330

34Sa

mp

lesi

zen

318

318

319

318

Sign

ica

nt

at10

Sign

ica

ntat

5

aD

iffe

ren

ces

inu

nion

vs

nonu

nio

nco

ef

cien

tsw

ere

not

sign

ica

nt









BL

EV

E

ffe

cts

on

Wa

ge

sa

(A)

The

Eff

ect

ofSu

bind

ices

(1)

(2)

(3)

(4)

Ind

epen

den

tV

aria

ble

BSE

BSE

BSE

BSE

Dep

end

ent

Var

iabl

e=

log(

Wag

esof

Uns

kille

dW

orke

rs)

Un

ioni

zed

shop

wor

kers

010

50

026

012

40

025

009

60

027

008

80

027

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

001

40

016

000

40

016

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

003

90

012

003

60

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

005

00

013

005

10

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

90

018

001

00

021

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

000

00

017

000

80

017

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

00

018

005

00

019

R2

050

40

516

050

80

513

F-t

est

on3

subi

nd

ices

inu

nion

wor

kpla

ces

410

1F

-tes

ton

3su

bin

dic

esin

non

un

ion

wor

kpla

ces

414

6Sa

mp

lesi

zen

319

318

319

318

Dep

end

ent

vari

able

=E

mp

loye

eTu

rnov

er

Un

ioni

zed

shop

wor

kers

134

21

100

144

51

036

115

01

173

111

41

234

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

026

70

602

017

80

680

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

009

00

543

009

90

529

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

108

30

557

122

70

658

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

018

81

201

059

61

417

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

046

90

707

064

40

737

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

081

90

540

103

20

854


R2

009

20

100

009

50

169

F-t

est

on3

subi

nd

ices

inu

nion

wor

kpla

ces

050

4F

-tes

ton

3su

bin

dic

esin

non

un

ion

wor

kpla

ces

121

3Sa

mp

lesi

zen

309

309

309

309

(B)

Th

eE

ffec

tof

Sup

por

tive

HR

Pra

ctic

es

Dep

end

ent

Var

iabl

elo

g(W

ages

ofU

nski

lled

Wor

kers

)

(1)

(2)

Ind

epen

den

tV

aria

ble

BSE

BSE

Un

ioni

zed

shop

wor

kers

010

10

027

010

70

028

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

005

00

013

005

10

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

30

020

000

90

021

Rep

rese

ntat

ive

emp

loye

ein

vno

nu

nion

000

90

017

001

10

017

Rep

rese

ntat

ive

emp

loye

ein

vu

nion

005

10

020

005

20

021

Con

ting

ent

pay

non

uni

on( z

)0

020

001

5C

onti

ngen

tp

ayu

nio

n( z

)0

037

001

8E

mp

loym

ent

secu

rity

Non

uni

on( z

)0

020

001

6E

mp

loym

ent

secu

rity

Uni

on( z

)0

023

001

6

R2

052

80

542

Sam

ple

size

n31

931

9

Not

eA

llre

gres

sion

sco

ntr

olle

dfo

rw

orkf

orce

aver

age

age

ad

um

my

for

Can

ada

the

per

cent

age

ofth

ew

orkf

orce

wit

ha

high

-sch

ool

dip

lom

alo

g(em

plo

ymen

t)

log(

mac

hin

es

pro

du

ctio

nw

orke

rs)

the

per

cent

age

ofth

ew

orkf

orce

mal

ean

dth

ere

gion

alp

rice

ind

ex

aA

llre

gres

sion

sin

clu

de

the

base

line

con

trol

sfr

omTa

ble

IID

iffe

ren

ces

inu

nion

vs

non

uni

onco

ef

cien

tsw

ere

not

sign

ica

nt


TA

BL

EV

IT

he

Eff

ects

of

Cl

us

ter

so

nW

ag

es

a

log(

Un

skil

led

Wag

es)

log(

Uns

kill

edC

omp

ensa

tion

)R

elat

ive

Com

pen

sati

onL

abor

Turn

over

Rat

e

BSE

BSE

BSE

BSE

(Con

stan

t)0

861

018

90

942

022

61

235

122

226

387

947

0A

vera

geag

e0

010

000

20

013

000

20

036

001

50

204

008

6C

anad

a0

352

003

90

286

004

20

062

026

53

344

201

3

wor

kfor

cew

ith

HS

dip

lom

a0

019

000

10

002

000

10

006

000

40

074

003

5lo

g(em

plo

ymen

t)0

060

001

40

075

001

70

271

008

80

828

059

2lo

g(m

achi

nes

pro

dn

wor

kers

)0

005

001

30

003

001

50

052

007

31

118

063

0

wor

kfor

cem

ale

000

40

000

000

40

001

000

90

003

001

30

021

Reg

iona

lp

rice

ind

ex0

184

014

10

182

016

50

405

088

92

465

625

1N

onu

nion

hig

h-in

volv

emen

tcl

ust

er0

090

003

20

105

003

60

306

021

00

325

123

2U

nio

nlo

w-i

nvo

lvem

ent

clu

ster

015

20

038

017

70

045

026

50

254

277

71

265

Un

ion

hig

h-in

volv

emen

tcl

ust

er0

196

003

50

242

004

00

642

023

60

852

137

8

R2

051

10

467

015

10

095

F-t

est

ofth

eeq

ual

ity

ofbo

ld10

910

120

642

466

097

2co

ef

cien

tsSa

mp

lesi

zen

319

319

305

309

Sign

ica

nt

at10

Si

gni

cant

at5

Not

eC

lust

ers

are

de

ned

inTa

ble

III(

B)

Bas

elin

e(o

mit

ted

)cl

ust

eris

non

uni

onan

dlo

win

volv

emen

ta

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant











BL

EV

II

Tes

ts

of

Th

eo

rie

s

(A)

Doe

sTr

ain

ing

Exp

lain

the

Wag

e-In

volv

emen

tR

elat

ion

a

All

Pla

nts

(1)

(2)

(3)

(4)

Dir

ect

EIb

ln(U

nski

lled

Wag

es)

ln(U

nsk

illed

Wag

es)

ln(U

nski

lled

Wag

es)

Sam

ple

BSE

BSE

BSE

BSE

Un

ion

008

90

122

012

40

027

011

80

024

012

20

024

Ind

exof

trai

ning

( z)

non

un

ion

008

40

087

001

30

019

000

80

018

Ind

exof

trai

ning

( z)

un

ion

008

10

068

001

50

010

001

40

010

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

005

00

013

004

90

012

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

90

020

001

70

019

R2

012

00

516

049

80

517

F-t

est

ofth

eeq

ual

ity

ofbo

ldco

ef

cien

ts1

157

656

80

719

353

3Sa

mp

lesi

zen

332

319

319

319

Pla

nts

wit

hou

tR

ecen

tH

irin

g

(5)

(6)

(7)

ln(U

nsk

ille

dW

age)

ln(U

nski

lled

Wag

e)ln

(Un

skil

led

Wag

e)

Sam

ple

BSE

BSE

BSE

Un

ion

009

90

138

004

50

026

007

60

027

Ind

exof

trai

ning

( z)

non

un

ion

000

30

021

004

00

019

Ind

exof

trai

ning

( z)

un

ion

005

80

009

005

40

009

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

006

60

024

007

10

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

006

50

012

005

10

021

R2

061

30

586

062

2F

-tes

tof

the

equ

alit

yof

bold

coef

ci

ents

136

90

312

081

3Sa

mp

lesi

zen

8686

86


(B)

Ef

cien

cyW

ages

and

Sup

ervi

sion

Turn

over

Turn

over

(1)

(2)

Who

leSa

mp

leB

SEB

SE

Un

ion

044

61

308

088

61

350

z(Tr

ain)

087

90

532

079

20

545

z(D

irec

t)0

019

060

9ln

(uns

kill

edw

age)

123

602

860

R2

010

10

171

Sam

ple

size

n29

529

5

Th

eE

ffec

tof

Sup

ervi

sion

Doe

sL

oyal

tyM

edia

te

Dir

ect

EIc

ln(U

nsk

ille

dW

ages

)D

irec

tE

Icln

(Un

skil

led

Wag

es)

Who

leSa

mp

leB

SEB

SEB

SEB

SE

Un

ion

006

90

120

012

40

025

005

50

116

012

50

026

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

60

013

005

20

021

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

50

018

001

70

014

Ind

exof

mgt

re

por

tsof

wor

ker

loya

lty

( z)

nonu

nio

n0

420

006

60

009

002

4In

dex

ofm

gt

rep

orts

ofw

orke

rlo

yalt

y( z

)u

nion

034

60

100

000

10

017

Sup

ervi

sors

w

orke

r1

168

037

20

211

011

3

F-t

est

onbo

thlo

yalt

yin

dic

es5

983

461

727

633

048

4(a

ndsu

per

viso

rsh

opw

orke

r)

R2

013

20

525

024

50

516

Sam

ple

size

n33

031

733

231

9

(con

tinu

ed)


TA

BL

EV

II

(co

ntin

ue

d)

(C)

Wag

esan

dIn

tens

ive

Em

plo

yee

Invo

lvem

enta

Dep

end

ent

Var

iabl

elo

g(U

nsk

ille

dW

age)

Ind

epen

den

tV

aria

ble

BSE

Un

ioni

zed

shop

wor

kers

009

940

031

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

000

870

018

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

003

90

019

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

80

016

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

70

022

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

001

40

018

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

40

020

Du

mm

yfo

rin

tens

ive

EI

and

non

uni

on0

038

004

8D

um

my

for

inte

nsiv

eE

Ian

du

nio

n0

033

006

1

R2

053

6F

-sta

tist

icon

incl

usi

onof

du

mm

yva

riab

les

046

1Sa

mp

lesi

zen

319

Sign

ica

ntat

10

Si

gni

cant

at5

a

All

regr

essi

ons

cont

roll

edfo

rw

orkf

orce

aver

age

age

ad

um

my

for

Can

ada

the

per

cent

age

ofth

ew

orkf

orce

wit

ha

hig

h-sc

hoo

ld

iplo

ma

log(

emp

loym

ent)

lo

g(m

ach

ines

p

rod

uct

ion

wor

kers

)th

ep

erce

ntag

eof

the

wor

kfor

cem

ale

and

the

regi

onal

pri

cein

dex

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant

b

Th

eco

ef

cien

tson

dir

ect

EI

do

not

chan

geby

ast

atis

tica

llysi

gni

can

tam

oun

tw

hen

the

ind

ices

oftr

aini

ngar

ead

ded

c

Th

eco

ef

cien

tson

dir

ect

emp

loye

ein

volv

emen

td

on

otch

ange

bya

stat

isti

cally

sign

ica

ntam

ount

wh

enth

ein

dic

esgi

ftsu

per

visi

onor

oflo

yalt

yar

ead

ded

[com

par

ing

colu

mn

(2)

or(4

)he

rean

dth

eco

rres

pon

din

gre

gres

sion

inTa

ble

V]


























TA

BL

EV

III

Eff

ec

tso

fE

mpl

oy

ee

Inv

ol

ve

me

nt

on

Em

plo

ym

en

tG

ro

wth

1

99

3ndash1

99

9

(1)

(2)

(3)

OL

SE

mpl

oym

ent

1999

OL

SE

mp

loym

ent

1999

Log

itS

urv

ival

Sam

ple

Fil

ms

Stil

lin

Bu

sine

ssFu

llSa

mp

leFu

llSa

mp

lein

2000

BSE

DP

DX

SEB

SE

(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

20

149

000

20

001

174

40

174

Em

plo

ymen

t19

93sq

uar

ed0

000

000

09

00E

076

00E

075

57E

041

92E

04C

anad

a6

824

100

570

121

30

571

108

117

956

9A

vera

geag

e4

125

502

50

044

002

61

628

471

3

wor

kfor

cew

ith

HS

dip

lom

a2

126

147

40

010

000

72

427

151

1

wor

kfor

cem

ale

045

11

290

000

60

007

207

61

15R

egio

nal

pri

cein

dex

348

110

324

540

283

11

692

527

4327

414

3U

nio

nize

dsh

opw

orke

rs29

640

675

340

046

034

654

479

625

12lo

g(m

achi

nes

pro

du

ctio

nw

orke

rs)

298

5432

069

025

90

155

183

427

698

Ind

exof

dir

ect

invo

lvem

ent

( z)

uni

on12

566

510

020

275

023

210

447

437

99In

dex

ofd

irec

tin

volv

emen

t( z

)no

nuni

on22

589

352

710

156

019

552

953

292

47

R2

054

777

54

corr

ectl

ycl

assi

ed

068

8Sa

mp

lesi

zen

128

332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant











































References


















































































22 Basic Hypotheses













and










and




and

























4 Data and Methods
















































TA

BL

EII

B

as

el

ine

Wa

ge

Eq

uatio

ns

(1)

(2)

(3)

(4)

ln(u

nsk

illed

wag

es)

ln(u

nski

lled

com

pen

sati

on)

Rel

ativ

eC

omp

ensa

tion

Lab

orTu

rnov

erR

ate

BSE

BSE

BSE

BSE

(Con

stan

t)0

859

018

20

968

021

21

266

121

327

957

806

6A

vera

geag

e0

010

000

20

012

000

20

034

001

30

204

008

4C

anad

a0

361

003

90

295

004

50

039

025

23

819

166

0

wor

kfor

cew

ith

HS

dip

lom

a0

002

000

10

002

000

10

006

000

40

070

002

5lo

g(em

plo

ymen

t)0

069

001

30

085

001

50

314

008

40

861

056

3lo

g(m

achi

nes

pro

dn

wor

kers

)0

007

001

20

004

001

40

063

007

81

240

052

3

wor

kfor

cem

ale

000

40

000

000

40

001

000

80

003

001

40

022

Reg

iona

lp

rice

ind

ex0

216

013

20

198

015

30

395

089

04

392

585

4U

nio

nize

dsh

opw

orke

rs0

116

002

60

142

003

00

281

017

11

363

113

9

R2

049

70

449

013

90

100

Sam

ple

size

n31

931

930

530

9

Sign

ica

nt

at10

Sign

ica

ntat

5



51 Incidence








TA

BL

EII

IIn

cid

en

ce

of

Dir

ec

tE

mpl

oy

ee

Inv

ol

ve

me

nt

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

(Con

stan

t)0

835

085

80

872

086

80

850

085

40

547

084

90

429

081

7A

vera

geag

e0

026

001

00

024

000

90

025

000

90

030

000

90

027

000

9C

anad

a0

416

016

20

404

017

50

436

017

30

395

017

10

330

016

4

wor

kfor

cew

ith

HS

dip

lom

a0

006

000

30

006

000

30

005

000

30

005

000

30

004

000

3lo

g(em

plo

ymen

t)0

257

005

40

256

006

00

251

005

90

238

005

90

207

005

7

log(

mac

hine

sp

rod

nw

orke

rs)

001

30

051

001

40

056

000

00

056

000

80

055

001

00

053

w

orkf

orce

mal

e0

001

000

20

002

000

20

002

000

20

003

000

20

003

000

2R

egio

nal

pri

cein

dex

003

80

673

001

20

632

009

40

622

011

40

616

024

00

592

Un

ioni

zed

shop

wor

kers

007

20

120

008

90

121

004

70

119

029

90

128

040

30

126

Ind

exof

trai

ning

( z)

008

20

054

(con

tinu

ed)


TA

BL

EII

I(c

on

tin

ue

d)

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

Ind

exof

emp

loym

ent

secu

rity

( z)

uni

on0

195

008

5P

revi

ous

non

un

ion

021

90

067

Supp

orti

veH

Rp

ract

ices

( z)

uni

on0

170

008

20

175

007

8P

revi

ous

non

un

ion

033

50

103

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

031

70

081

032

10

069

Pre

viou

sn

onu

nio

n0

131

007

3

R2

011

40

120

015

60

168

023

7F

-tes

tof

the

equ

alit

yof

bold

coef

ci

ents

805

103

7014

566

Sam

ple

size

n33

233

233

233

233

2


(B)

Clu

ster

An

alys

isof

the

Pla

nts

Uni

on

Uni

on

Non

un

ion

N

onu

nio

n

AN

OV

AH

igh

Invo

lve

Low

Invo

lve

Hig

hIn

volv

eL

owIn

volv

eSi

ga

Var

iabl

esU

sed

toC

reat

eth

eC

lust

ers

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

III

III

Ind

exof

com

mit

tee

037

070

062

008

070

104

010

000

096

027

000

089

00

020

164

exis

ten

ce( z

)In

dex

ofd

irec

t0

600

530

490

960

790

720

670

650

521

050

920

680

120

1E20

invo

lvem

ent

( z)

Ind

exof

job

secu

rity

( z)

003

078

092

027

093

100

022

078

101

012

093

102

007

004

001

Ind

exof

grie

van

cep

lans

( z)

092

124

060

087

124

061

045

108

083

077

108

057

00

570

001

Sup

por

tive

HR

pra

ctic

es( z

)0

460

421

080

110

040

830

050

040

940

500

630

860

002

4E06

Ind

exof

imp

rove

men

t0

220

380

670

250

381

230

340

380

330

660

381

430

830

2E16

pro

cess

( z)

Em

plo

yee

invo

lvem

ent

( z)

092

091

080

034

034

073

019

019

074

103

095

070

00

1E20

Ind

exof

wor

ker

grou

p0

710

900

740

690

890

800

480

300

700

870

890

710

160

1E20

in

uen

ceov

erp

olic

ies

( z)

Ind

exof

com

mit

tee

ampu

nio

n1

030

821

160

200

010

890

320

830

680

560

830

480

00

003

in

uen

ceov

er6

pol

icie

s( z

)In

dex

ofim

pro

vem

ent

033

017

073

082

097

095

044

074

072

048

040

104

00

1E15

thro

ugh

list

enin

g( z

)

Ind

exof

cont

inge

ntp

ay( z

)0

230

270

890

261

020

900

340

271

070

070

270

910

083

000

2In

dex

ofre

pre

sent

ativ

e0

900

920

910

180

180

940

270

080

860

530

540

760

00

014

invo

lvem

ent

( z)

(con

tinu

ed)


TA

BL

EII

I(c

on

tin

ue

d)

Un

ion

U

nion

N

onu

nion

N

onu

nion

A

NO

VA

Hig

hIn

volv

eL

owIn

volv

eH

igh

Invo

lve

Low

Invo

lve

Sig

a

Var

iabl

esU

sed

toC

reat

eth

eC

lust

ers

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

III

III

Ind

exof

task

sp

erfo

rmed

041

034

069

059

038

084

040

034

086

067

066

091

061

05E

19by

sem

iski

lled

wor

kers

( z)

Ind

exof

use

ofte

ams

( z)

010

000

070

041

000

109

033

044

067

036

000

113

002

02E

09In

dex

oftr

aini

ng( z

)0

180

001

350

120

040

610

010

060

610

050

220

890

40

070

709

Oth

erV

aria

bles

Ave

rage

age

383

339

00

636

388

638

50

652

331

833

00

570

352

835

00

585

00

610

008

Can

ada

019

000

040

024

000

043

009

000

029

008

000

028

00

430

821

w

orkf

orce

wit

hH

S78

69

850

020

03

701

480

00

260

881

11

900

023

61

796

580

00

196

70

020

020

631

dip

lom

alo

g(em

plo

ymen

t)5

605

530

835

385

551

025

305

330

964

864

871

140

013

000

1lo

g(m

achi

nes

pro

du

ctio

n1

891

831

132

021

771

071

811

701

061

481

440

900

020

460

02w

orke

rs)

w

orkf

orce

mal

e62

87

650

024

36

628

265

00

267

355

64

550

022

23

604

660

00

232

50

030

990

108

Reg

iona

lp

rice

ind

ex1

051

070

081

061

090

081

041

010

091

041

000

110

010

250

963

log(

com

pen

sati

onof

296

294

027

289

287

036

282

284

022

275

278

026

00

180

027

skil

led

)


log(

wag

esof

skill

ed)

265

264

024

260

264

035

255

256

021

250

248

024

00

30

049

log(

com

pen

sati

onof

un

skill

ed)

271

269

031

265

263

036

246

243

025

239

240

027

00

280

07

log(

wag

esof

uns

kille

d)

240

238

028

236

233

034

219

216

024

214

214

024

00

40

099

Rel

ativ

eco

mp

ensa

tion

507

500

151

476

450

123

463

400

131

439

400

139

00

170

175

Lab

ortu

rnov

erra

te5

212

008

623

822

005

237

364

0010

19

744

500

806

00

260

948

log(

tota

lno

em

plo

yees

1999

0

070

110

540

050

110

700

210

190

620

350

240

910

010

870

191

tota

lno

em

plo

yees

1992

)b

log(

tota

lno

em

plo

yees

1999

0

040

030

130

25to

tal

no

emp

loye

es19

92)c

Ou

tof

busi

ness

by19

990

170

000

380

180

000

390

160

000

370

090

000

290

210

920

13

Nu

mbe

rof

pla

nts

incl

ust

er98

7817

395

Sign

ica

nt

at10

Sign

ica

ntat

5

aI

Un

ion

vs

nonu

nio

n

rms

II

Low

-vs

hig

h-in

volv

emen

tu

nio

ncl

ust

ers

III

Low

-vs

hig

h-in

volv

emen

tno

nuni

oncl

ust

ers

bC

ond

itio

nal

onbe

ing

stil

lin

busi

nes

sin

1999

c

Em

plo

ymen

teq

ual

sn

egat

ive

in

nit

yfo

r

rms

that

shu

td

own

thu

son

lyth

em

edia

nis

wel

ld

en

ed


TA

BL

EIV

T

he

Ef

fe

ct

of

th

eS

um

ma

ry

Ind

ex

on

Wa

ge

sa

nd

Tu

rn

ov

er

a

(1)

(2)

(3)

(4)

ln(U

nski

lled

Wag

es)

ln(U

nski

lled

Com

pen

sati

on)

Rel

ativ

eC

ompe

nsa

tion

Turn

over

Rat

e

Var

iabl

eB

SEB

SEB

SEB

SE

(Con

stan

t)0

964

018

01

065

022

61

711

120

625

910

967

1A

vera

geag

e0

001

000

20

012

000

20

033

001

50

213

008

8C

anad

a0

345

003

80

279

004

50

122

025

83

158

206

6

wor

kfor

cew

ith

HS

dip

lom

a0

002

000

10

002

000

10

004

000

40

068

003

5lo

g(em

plo

ymen

t)0

059

001

30

075

001

60

253

008

80

692

059

5lo

g(m

achi

nes

pro

dn

wor

kers

)0

008

001

20

007

001

40

068

007

31

092

062

4

wor

kfor

cem

ale

000

30

000

000

40

001

000

80

003

001

20

021

Reg

iona

lp

rice

ind

ex0

208

013

00

207

016

80

275

088

33

145

625

8U

nio

nize

dsh

opw

orke

rs0

093

002

60

114

003

00

134

016

91

155

109

2E

mp

loye

ein

volv

emen

t( z

)no

nun

ion

003

60

015

003

30

015

018

90

111

051

40

607

Em

plo

yee

invo

lvem

ent

( z)

uni

on0

052

001

80

069

001

80

287

012

00

335

059

7

R2

051

60

473

016

30

093

F-t

est

ofE

Iin

dex

and

its

inte

ract

ion

wit

hu

nio

n6

807

234

330

34Sa

mp

lesi

zen

318

318

319

318

Sign

ica

nt

at10

Sign

ica

ntat

5

aD

iffe

ren

ces

inu

nion

vs

nonu

nio

nco

ef

cien

tsw

ere

not

sign

ica

nt









BL

EV

E

ffe

cts

on

Wa

ge

sa

(A)

The

Eff

ect

ofSu

bind

ices

(1)

(2)

(3)

(4)

Ind

epen

den

tV

aria

ble

BSE

BSE

BSE

BSE

Dep

end

ent

Var

iabl

e=

log(

Wag

esof

Uns

kille

dW

orke

rs)

Un

ioni

zed

shop

wor

kers

010

50

026

012

40

025

009

60

027

008

80

027

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

001

40

016

000

40

016

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

003

90

012

003

60

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

005

00

013

005

10

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

90

018

001

00

021

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

000

00

017

000

80

017

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

00

018

005

00

019

R2

050

40

516

050

80

513

F-t

est

on3

subi

nd

ices

inu

nion

wor

kpla

ces

410

1F

-tes

ton

3su

bin

dic

esin

non

un

ion

wor

kpla

ces

414

6Sa

mp

lesi

zen

319

318

319

318

Dep

end

ent

vari

able

=E

mp

loye

eTu

rnov

er

Un

ioni

zed

shop

wor

kers

134

21

100

144

51

036

115

01

173

111

41

234

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

026

70

602

017

80

680

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

009

00

543

009

90

529

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

108

30

557

122

70

658

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

018

81

201

059

61

417

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

046

90

707

064

40

737

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

081

90

540

103

20

854


R2

009

20

100

009

50

169

F-t

est

on3

subi

nd

ices

inu

nion

wor

kpla

ces

050

4F

-tes

ton

3su

bin

dic

esin

non

un

ion

wor

kpla

ces

121

3Sa

mp

lesi

zen

309

309

309

309

(B)

Th

eE

ffec

tof

Sup

por

tive

HR

Pra

ctic

es

Dep

end

ent

Var

iabl

elo

g(W

ages

ofU

nski

lled

Wor

kers

)

(1)

(2)

Ind

epen

den

tV

aria

ble

BSE

BSE

Un

ioni

zed

shop

wor

kers

010

10

027

010

70

028

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

005

00

013

005

10

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

30

020

000

90

021

Rep

rese

ntat

ive

emp

loye

ein

vno

nu

nion

000

90

017

001

10

017

Rep

rese

ntat

ive

emp

loye

ein

vu

nion

005

10

020

005

20

021

Con

ting

ent

pay

non

uni

on( z

)0

020

001

5C

onti

ngen

tp

ayu

nio

n( z

)0

037

001

8E

mp

loym

ent

secu

rity

Non

uni

on( z

)0

020

001

6E

mp

loym

ent

secu

rity

Uni

on( z

)0

023

001

6

R2

052

80

542

Sam

ple

size

n31

931

9

Not

eA

llre

gres

sion

sco

ntr

olle

dfo

rw

orkf

orce

aver

age

age

ad

um

my

for

Can

ada

the

per

cent

age

ofth

ew

orkf

orce

wit

ha

high

-sch

ool

dip

lom

alo

g(em

plo

ymen

t)

log(

mac

hin

es

pro

du

ctio

nw

orke

rs)

the

per

cent

age

ofth

ew

orkf

orce

mal

ean

dth

ere

gion

alp

rice

ind

ex

aA

llre

gres

sion

sin

clu

de

the

base

line

con

trol

sfr

omTa

ble

IID

iffe

ren

ces

inu

nion

vs

non

uni

onco

ef

cien

tsw

ere

not

sign

ica

nt


TA

BL

EV

IT

he

Eff

ects

of

Cl

us

ter

so

nW

ag

es

a

log(

Un

skil

led

Wag

es)

log(

Uns

kill

edC

omp

ensa

tion

)R

elat

ive

Com

pen

sati

onL

abor

Turn

over

Rat

e

BSE

BSE

BSE

BSE

(Con

stan

t)0

861

018

90

942

022

61

235

122

226

387

947

0A

vera

geag

e0

010

000

20

013

000

20

036

001

50

204

008

6C

anad

a0

352

003

90

286

004

20

062

026

53

344

201

3

wor

kfor

cew

ith

HS

dip

lom

a0

019

000

10

002

000

10

006

000

40

074

003

5lo

g(em

plo

ymen

t)0

060

001

40

075

001

70

271

008

80

828

059

2lo

g(m

achi

nes

pro

dn

wor

kers

)0

005

001

30

003

001

50

052

007

31

118

063

0

wor

kfor

cem

ale

000

40

000

000

40

001

000

90

003

001

30

021

Reg

iona

lp

rice

ind

ex0

184

014

10

182

016

50

405

088

92

465

625

1N

onu

nion

hig

h-in

volv

emen

tcl

ust

er0

090

003

20

105

003

60

306

021

00

325

123

2U

nio

nlo

w-i

nvo

lvem

ent

clu

ster

015

20

038

017

70

045

026

50

254

277

71

265

Un

ion

hig

h-in

volv

emen

tcl

ust

er0

196

003

50

242

004

00

642

023

60

852

137

8

R2

051

10

467

015

10

095

F-t

est

ofth

eeq

ual

ity

ofbo

ld10

910

120

642

466

097

2co

ef

cien

tsSa

mp

lesi

zen

319

319

305

309

Sign

ica

nt

at10

Si

gni

cant

at5

Not

eC

lust

ers

are

de

ned

inTa

ble

III(

B)

Bas

elin

e(o

mit

ted

)cl

ust

eris

non

uni

onan

dlo

win

volv

emen

ta

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant











BL

EV

II

Tes

ts

of

Th

eo

rie

s

(A)

Doe

sTr

ain

ing

Exp

lain

the

Wag

e-In

volv

emen

tR

elat

ion

a

All

Pla

nts

(1)

(2)

(3)

(4)

Dir

ect

EIb

ln(U

nski

lled

Wag

es)

ln(U

nsk

illed

Wag

es)

ln(U

nski

lled

Wag

es)

Sam

ple

BSE

BSE

BSE

BSE

Un

ion

008

90

122

012

40

027

011

80

024

012

20

024

Ind

exof

trai

ning

( z)

non

un

ion

008

40

087

001

30

019

000

80

018

Ind

exof

trai

ning

( z)

un

ion

008

10

068

001

50

010

001

40

010

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

005

00

013

004

90

012

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

90

020

001

70

019

R2

012

00

516

049

80

517

F-t

est

ofth

eeq

ual

ity

ofbo

ldco

ef

cien

ts1

157

656

80

719

353

3Sa

mp

lesi

zen

332

319

319

319

Pla

nts

wit

hou

tR

ecen

tH

irin

g

(5)

(6)

(7)

ln(U

nsk

ille

dW

age)

ln(U

nski

lled

Wag

e)ln

(Un

skil

led

Wag

e)

Sam

ple

BSE

BSE

BSE

Un

ion

009

90

138

004

50

026

007

60

027

Ind

exof

trai

ning

( z)

non

un

ion

000

30

021

004

00

019

Ind

exof

trai

ning

( z)

un

ion

005

80

009

005

40

009

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

006

60

024

007

10

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

006

50

012

005

10

021

R2

061

30

586

062

2F

-tes

tof

the

equ

alit

yof

bold

coef

ci

ents

136

90

312

081

3Sa

mp

lesi

zen

8686

86


(B)

Ef

cien

cyW

ages

and

Sup

ervi

sion

Turn

over

Turn

over

(1)

(2)

Who

leSa

mp

leB

SEB

SE

Un

ion

044

61

308

088

61

350

z(Tr

ain)

087

90

532

079

20

545

z(D

irec

t)0

019

060

9ln

(uns

kill

edw

age)

123

602

860

R2

010

10

171

Sam

ple

size

n29

529

5

Th

eE

ffec

tof

Sup

ervi

sion

Doe

sL

oyal

tyM

edia

te

Dir

ect

EIc

ln(U

nsk

ille

dW

ages

)D

irec

tE

Icln

(Un

skil

led

Wag

es)

Who

leSa

mp

leB

SEB

SEB

SEB

SE

Un

ion

006

90

120

012

40

025

005

50

116

012

50

026

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

60

013

005

20

021

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

50

018

001

70

014

Ind

exof

mgt

re

por

tsof

wor

ker

loya

lty

( z)

nonu

nio

n0

420

006

60

009

002

4In

dex

ofm

gt

rep

orts

ofw

orke

rlo

yalt

y( z

)u

nion

034

60

100

000

10

017

Sup

ervi

sors

w

orke

r1

168

037

20

211

011

3

F-t

est

onbo

thlo

yalt

yin

dic

es5

983

461

727

633

048

4(a

ndsu

per

viso

rsh

opw

orke

r)

R2

013

20

525

024

50

516

Sam

ple

size

n33

031

733

231

9

(con

tinu

ed)


TA

BL

EV

II

(co

ntin

ue

d)

(C)

Wag

esan

dIn

tens

ive

Em

plo

yee

Invo

lvem

enta

Dep

end

ent

Var

iabl

elo

g(U

nsk

ille

dW

age)

Ind

epen

den

tV

aria

ble

BSE

Un

ioni

zed

shop

wor

kers

009

940

031

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

000

870

018

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

003

90

019

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

80

016

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

70

022

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

001

40

018

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

40

020

Du

mm

yfo

rin

tens

ive

EI

and

non

uni

on0

038

004

8D

um

my

for

inte

nsiv

eE

Ian

du

nio

n0

033

006

1

R2

053

6F

-sta

tist

icon

incl

usi

onof

du

mm

yva

riab

les

046

1Sa

mp

lesi

zen

319

Sign

ica

ntat

10

Si

gni

cant

at5

a

All

regr

essi

ons

cont

roll

edfo

rw

orkf

orce

aver

age

age

ad

um

my

for

Can

ada

the

per

cent

age

ofth

ew

orkf

orce

wit

ha

hig

h-sc

hoo

ld

iplo

ma

log(

emp

loym

ent)

lo

g(m

ach

ines

p

rod

uct

ion

wor

kers

)th

ep

erce

ntag

eof

the

wor

kfor

cem

ale

and

the

regi

onal

pri

cein

dex

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant

b

Th

eco

ef

cien

tson

dir

ect

EI

do

not

chan

geby

ast

atis

tica

llysi

gni

can

tam

oun

tw

hen

the

ind

ices

oftr

aini

ngar

ead

ded

c

Th

eco

ef

cien

tson

dir

ect

emp

loye

ein

volv

emen

td

on

otch

ange

bya

stat

isti

cally

sign

ica

ntam

ount

wh

enth

ein

dic

esgi

ftsu

per

visi

onor

oflo

yalt

yar

ead

ded

[com

par

ing

colu

mn

(2)

or(4

)he

rean

dth

eco

rres

pon

din

gre

gres

sion

inTa

ble

V]


























TA

BL

EV

III

Eff

ec

tso

fE

mpl

oy

ee

Inv

ol

ve

me

nt

on

Em

plo

ym

en

tG

ro

wth

1

99

3ndash1

99

9

(1)

(2)

(3)

OL

SE

mpl

oym

ent

1999

OL

SE

mp

loym

ent

1999

Log

itS

urv

ival

Sam

ple

Fil

ms

Stil

lin

Bu

sine

ssFu

llSa

mp

leFu

llSa

mp

lein

2000

BSE

DP

DX

SEB

SE

(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

20

149

000

20

001

174

40

174

Em

plo

ymen

t19

93sq

uar

ed0

000

000

09

00E

076

00E

075

57E

041

92E

04C

anad

a6

824

100

570

121

30

571

108

117

956

9A

vera

geag

e4

125

502

50

044

002

61

628

471

3

wor

kfor

cew

ith

HS

dip

lom

a2

126

147

40

010

000

72

427

151

1

wor

kfor

cem

ale

045

11

290

000

60

007

207

61

15R

egio

nal

pri

cein

dex

348

110

324

540

283

11

692

527

4327

414

3U

nio

nize

dsh

opw

orke

rs29

640

675

340

046

034

654

479

625

12lo

g(m

achi

nes

pro

du

ctio

nw

orke

rs)

298

5432

069

025

90

155

183

427

698

Ind

exof

dir

ect

invo

lvem

ent

( z)

uni

on12

566

510

020

275

023

210

447

437

99In

dex

ofd

irec

tin

volv

emen

t( z

)no

nuni

on22

589

352

710

156

019

552

953

292

47

R2

054

777

54

corr

ectl

ycl

assi

ed

068

8Sa

mp

lesi

zen

128

332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant











































References




















































and










and




and

























4 Data and Methods
















































TA

BL

EII

B

as

el

ine

Wa

ge

Eq

uatio

ns

(1)

(2)

(3)

(4)

ln(u

nsk

illed

wag

es)

ln(u

nski

lled

com

pen

sati

on)

Rel

ativ

eC

omp

ensa

tion

Lab

orTu

rnov

erR

ate

BSE

BSE

BSE

BSE

(Con

stan

t)0

859

018

20

968

021

21

266

121

327

957

806

6A

vera

geag

e0

010

000

20

012

000

20

034

001

30

204

008

4C

anad

a0

361

003

90

295

004

50

039

025

23

819

166

0

wor

kfor

cew

ith

HS

dip

lom

a0

002

000

10

002

000

10

006

000

40

070

002

5lo

g(em

plo

ymen

t)0

069

001

30

085

001

50

314

008

40

861

056

3lo

g(m

achi

nes

pro

dn

wor

kers

)0

007

001

20

004

001

40

063

007

81

240

052

3

wor

kfor

cem

ale

000

40

000

000

40

001

000

80

003

001

40

022

Reg

iona

lp

rice

ind

ex0

216

013

20

198

015

30

395

089

04

392

585

4U

nio

nize

dsh

opw

orke

rs0

116

002

60

142

003

00

281

017

11

363

113

9

R2

049

70

449

013

90

100

Sam

ple

size

n31

931

930

530

9

Sign

ica

nt

at10

Sign

ica

ntat

5



51 Incidence








TA

BL

EII

IIn

cid

en

ce

of

Dir

ec

tE

mpl

oy

ee

Inv

ol

ve

me

nt

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

(Con

stan

t)0

835

085

80

872

086

80

850

085

40

547

084

90

429

081

7A

vera

geag

e0

026

001

00

024

000

90

025

000

90

030

000

90

027

000

9C

anad

a0

416

016

20

404

017

50

436

017

30

395

017

10

330

016

4

wor

kfor

cew

ith

HS

dip

lom

a0

006

000

30

006

000

30

005

000

30

005

000

30

004

000

3lo

g(em

plo

ymen

t)0

257

005

40

256

006

00

251

005

90

238

005

90

207

005

7

log(

mac

hine

sp

rod

nw

orke

rs)

001

30

051

001

40

056

000

00

056

000

80

055

001

00

053

w

orkf

orce

mal

e0

001

000

20

002

000

20

002

000

20

003

000

20

003

000

2R

egio

nal

pri

cein

dex

003

80

673

001

20

632

009

40

622

011

40

616

024

00

592

Un

ioni

zed

shop

wor

kers

007

20

120

008

90

121

004

70

119

029

90

128

040

30

126

Ind

exof

trai

ning

( z)

008

20

054

(con

tinu

ed)


TA

BL

EII

I(c

on

tin

ue

d)

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

Ind

exof

emp

loym

ent

secu

rity

( z)

uni

on0

195

008

5P

revi

ous

non

un

ion

021

90

067

Supp

orti

veH

Rp

ract

ices

( z)

uni

on0

170

008

20

175

007

8P

revi

ous

non

un

ion

033

50

103

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

031

70

081

032

10

069

Pre

viou

sn

onu

nio

n0

131

007

3

R2

011

40

120

015

60

168

023

7F

-tes

tof

the

equ

alit

yof

bold

coef

ci

ents

805

103

7014

566

Sam

ple

size

n33

233

233

233

233

2


(B)

Clu

ster

An

alys

isof

the

Pla

nts

Uni

on

Uni

on

Non

un

ion

N

onu

nio

n

AN

OV

AH

igh

Invo

lve

Low

Invo

lve

Hig

hIn

volv

eL

owIn

volv

eSi

ga

Var

iabl

esU

sed

toC

reat

eth

eC

lust

ers

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

III

III

Ind

exof

com

mit

tee

037

070

062

008

070

104

010

000

096

027

000

089

00

020

164

exis

ten

ce( z

)In

dex

ofd

irec

t0

600

530

490

960

790

720

670

650

521

050

920

680

120

1E20

invo

lvem

ent

( z)

Ind

exof

job

secu

rity

( z)

003

078

092

027

093

100

022

078

101

012

093

102

007

004

001

Ind

exof

grie

van

cep

lans

( z)

092

124

060

087

124

061

045

108

083

077

108

057

00

570

001

Sup

por

tive

HR

pra

ctic

es( z

)0

460

421

080

110

040

830

050

040

940

500

630

860

002

4E06

Ind

exof

imp

rove

men

t0

220

380

670

250

381

230

340

380

330

660

381

430

830

2E16

pro

cess

( z)

Em

plo

yee

invo

lvem

ent

( z)

092

091

080

034

034

073

019

019

074

103

095

070

00

1E20

Ind

exof

wor

ker

grou

p0

710

900

740

690

890

800

480

300

700

870

890

710

160

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lty

( z)

nonu

nio

n0

420

006

60

009

002

4In

dex

ofm

gt

rep

orts

ofw

orke

rlo

yalt

y( z

)u

nion

034

60

100

000

10

017

Sup

ervi

sors

w

orke

r1

168

037

20

211

011

3

F-t

est

onbo

thlo

yalt

yin

dic

es5

983

461

727

633

048

4(a

ndsu

per

viso

rsh

opw

orke

r)

R2

013

20

525

024

50

516

Sam

ple

size

n33

031

733

231

9

(con

tinu

ed)


TA

BL

EV

II

(co

ntin

ue

d)

(C)

Wag

esan

dIn

tens

ive

Em

plo

yee

Invo

lvem

enta

Dep

end

ent

Var

iabl

elo

g(U

nsk

ille

dW

age)

Ind

epen

den

tV

aria

ble

BSE

Un

ioni

zed

shop

wor

kers

009

940

031

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

000

870

018

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

003

90

019

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

80

016

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

70

022

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

001

40

018

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

40

020

Du

mm

yfo

rin

tens

ive

EI

and

non

uni

on0

038

004

8D

um

my

for

inte

nsiv

eE

Ian

du

nio

n0

033

006

1

R2

053

6F

-sta

tist

icon

incl

usi

onof

du

mm

yva

riab

les

046

1Sa

mp

lesi

zen

319

Sign

ica

ntat

10

Si

gni

cant

at5

a

All

regr

essi

ons

cont

roll

edfo

rw

orkf

orce

aver

age

age

ad

um

my

for

Can

ada

the

per

cent

age

ofth

ew

orkf

orce

wit

ha

hig

h-sc

hoo

ld

iplo

ma

log(

emp

loym

ent)

lo

g(m

ach

ines

p

rod

uct

ion

wor

kers

)th

ep

erce

ntag

eof

the

wor

kfor

cem

ale

and

the

regi

onal

pri

cein

dex

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant

b

Th

eco

ef

cien

tson

dir

ect

EI

do

not

chan

geby

ast

atis

tica

llysi

gni

can

tam

oun

tw

hen

the

ind

ices

oftr

aini

ngar

ead

ded

c

Th

eco

ef

cien

tson

dir

ect

emp

loye

ein

volv

emen

td

on

otch

ange

bya

stat

isti

cally

sign

ica

ntam

ount

wh

enth

ein

dic

esgi

ftsu

per

visi

onor

oflo

yalt

yar

ead

ded

[com

par

ing

colu

mn

(2)

or(4

)he

rean

dth

eco

rres

pon

din

gre

gres

sion

inTa

ble

V]


























TA

BL

EV

III

Eff

ec

tso

fE

mpl

oy

ee

Inv

ol

ve

me

nt

on

Em

plo

ym

en

tG

ro

wth

1

99

3ndash1

99

9

(1)

(2)

(3)

OL

SE

mpl

oym

ent

1999

OL

SE

mp

loym

ent

1999

Log

itS

urv

ival

Sam

ple

Fil

ms

Stil

lin

Bu

sine

ssFu

llSa

mp

leFu

llSa

mp

lein

2000

BSE

DP

DX

SEB

SE

(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

20

149

000

20

001

174

40

174

Em

plo

ymen

t19

93sq

uar

ed0

000

000

09

00E

076

00E

075

57E

041

92E

04C

anad

a6

824

100

570

121

30

571

108

117

956

9A

vera

geag

e4

125

502

50

044

002

61

628

471

3

wor

kfor

cew

ith

HS

dip

lom

a2

126

147

40

010

000

72

427

151

1

wor

kfor

cem

ale

045

11

290

000

60

007

207

61

15R

egio

nal

pri

cein

dex

348

110

324

540

283

11

692

527

4327

414

3U

nio

nize

dsh

opw

orke

rs29

640

675

340

046

034

654

479

625

12lo

g(m

achi

nes

pro

du

ctio

nw

orke

rs)

298

5432

069

025

90

155

183

427

698

Ind

exof

dir

ect

invo

lvem

ent

( z)

uni

on12

566

510

020

275

023

210

447

437

99In

dex

ofd

irec

tin

volv

emen

t( z

)no

nuni

on22

589

352

710

156

019

552

953

292

47

R2

054

777

54

corr

ectl

ycl

assi

ed

068

8Sa

mp

lesi

zen

128

332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant











































References




















































and




and

























4 Data and Methods
















































TA

BL

EII

B

as

el

ine

Wa

ge

Eq

uatio

ns

(1)

(2)

(3)

(4)

ln(u

nsk

illed

wag

es)

ln(u

nski

lled

com

pen

sati

on)

Rel

ativ

eC

omp

ensa

tion

Lab

orTu

rnov

erR

ate

BSE

BSE

BSE

BSE

(Con

stan

t)0

859

018

20

968

021

21

266

121

327

957

806

6A

vera

geag

e0

010

000

20

012

000

20

034

001

30

204

008

4C

anad

a0

361

003

90

295

004

50

039

025

23

819

166

0

wor

kfor

cew

ith

HS

dip

lom

a0

002

000

10

002

000

10

006

000

40

070

002

5lo

g(em

plo

ymen

t)0

069

001

30

085

001

50

314

008

40

861

056

3lo

g(m

achi

nes

pro

dn

wor

kers

)0

007

001

20

004

001

40

063

007

81

240

052

3

wor

kfor

cem

ale

000

40

000

000

40

001

000

80

003

001

40

022

Reg

iona

lp

rice

ind

ex0

216

013

20

198

015

30

395

089

04

392

585

4U

nio

nize

dsh

opw

orke

rs0

116

002

60

142

003

00

281

017

11

363

113

9

R2

049

70

449

013

90

100

Sam

ple

size

n31

931

930

530

9

Sign

ica

nt

at10

Sign

ica

ntat

5



51 Incidence








TA

BL

EII

IIn

cid

en

ce

of

Dir

ec

tE

mpl

oy

ee

Inv

ol

ve

me

nt

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

(Con

stan

t)0

835

085

80

872

086

80

850

085

40

547

084

90

429

081

7A

vera

geag

e0

026

001

00

024

000

90

025

000

90

030

000

90

027

000

9C

anad

a0

416

016

20

404

017

50

436

017

30

395

017

10

330

016

4

wor

kfor

cew

ith

HS

dip

lom

a0

006

000

30

006

000

30

005

000

30

005

000

30

004

000

3lo

g(em

plo

ymen

t)0

257

005

40

256

006

00

251

005

90

238

005

90

207

005

7

log(

mac

hine

sp

rod

nw

orke

rs)

001

30

051

001

40

056

000

00

056

000

80

055

001

00

053

w

orkf

orce

mal

e0

001

000

20

002

000

20

002

000

20

003

000

20

003

000

2R

egio

nal

pri

cein

dex

003

80

673

001

20

632

009

40

622

011

40

616

024

00

592

Un

ioni

zed

shop

wor

kers

007

20

120

008

90

121

004

70

119

029

90

128

040

30

126

Ind

exof

trai

ning

( z)

008

20

054

(con

tinu

ed)


TA

BL

EII

I(c

on

tin

ue

d)

(A)

Dep

end

ent

Var

iabl

eD

irec

tE

I

(1)

(2)

(3)

(4)

(5)

Var

iabl

eB

SEB

SEB

SEB

SEB

SE

Ind

exof

emp

loym

ent

secu

rity

( z)

uni

on0

195

008

5P

revi

ous

non

un

ion

021

90

067

Supp

orti

veH

Rp

ract

ices

( z)

uni

on0

170

008

20

175

007

8P

revi

ous

non

un

ion

033

50

103

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

031

70

081

032

10

069

Pre

viou

sn

onu

nio

n0

131

007

3

R2

011

40

120

015

60

168

023

7F

-tes

tof

the

equ

alit

yof

bold

coef

ci

ents

805

103

7014

566

Sam

ple

size

n33

233

233

233

233

2


(B)

Clu

ster

An

alys

isof

the

Pla

nts

Uni

on

Uni

on

Non

un

ion

N

onu

nio

n

AN

OV

AH

igh

Invo

lve

Low

Invo

lve

Hig

hIn

volv

eL

owIn

volv

eSi

ga

Var

iabl

esU

sed

toC

reat

eth

eC

lust

ers

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

III

III

Ind

exof

com

mit

tee

037

070

062

008

070

104

010

000

096

027

000

089

00

020

164

exis

ten

ce( z

)In

dex

ofd

irec

t0

600

530

490

960

790

720

670

650

521

050

920

680

120

1E20

invo

lvem

ent

( z)

Ind

exof

job

secu

rity

( z)

003

078

092

027

093

100

022

078

101

012

093

102

007

004

001

Ind

exof

grie

van

cep

lans

( z)

092

124

060

087

124

061

045

108

083

077

108

057

00

570

001

Sup

por

tive

HR

pra

ctic

es( z

)0

460

421

080

110

040

830

050

040

940

500

630

860

002

4E06

Ind

exof

imp

rove

men

t0

220

380

670

250

381

230

340

380

330

660

381

430

830

2E16

pro

cess

( z)

Em

plo

yee

invo

lvem

ent

( z)

092

091

080

034

034

073

019

019

074

103

095

070

00

1E20

Ind

exof

wor

ker

grou

p0

710

900

740

690

890

800

480

300

700

870

890

710

160

1E20

in

uen

ceov

erp

olic

ies

( z)

Ind

exof

com

mit

tee

ampu

nio

n1

030

821

160

200

010

890

320

830

680

560

830

480

00

003

in

uen

ceov

er6

pol

icie

s( z

)In

dex

ofim

pro

vem

ent

033

017

073

082

097

095

044

074

072

048

040

104

00

1E15

thro

ugh

list

enin

g( z

)

Ind

exof

cont

inge

ntp

ay( z

)0

230

270

890

261

020

900

340

271

070

070

270

910

083

000

2In

dex

ofre

pre

sent

ativ

e0

900

920

910

180

180

940

270

080

860

530

540

760

00

014

invo

lvem

ent

( z)

(con

tinu

ed)


TA

BL

EII

I(c

on

tin

ue

d)

Un

ion

U

nion

N

onu

nion

N

onu

nion

A

NO

VA

Hig

hIn

volv

eL

owIn

volv

eH

igh

Invo

lve

Low

Invo

lve

Sig

a

Var

iabl

esU

sed

toC

reat

eth

eC

lust

ers

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

Mea

nM

edia

nSD

III

III

Ind

exof

task

sp

erfo

rmed

041

034

069

059

038

084

040

034

086

067

066

091

061

05E

19by

sem

iski

lled

wor

kers

( z)

Ind

exof

use

ofte

ams

( z)

010

000

070

041

000

109

033

044

067

036

000

113

002

02E

09In

dex

oftr

aini

ng( z

)0

180

001

350

120

040

610

010

060

610

050

220

890

40

070

709

Oth

erV

aria

bles

Ave

rage

age

383

339

00

636

388

638

50

652

331

833

00

570

352

835

00

585

00

610

008

Can

ada

019

000

040

024

000

043

009

000

029

008

000

028

00

430

821

w

orkf

orce

wit

hH

S78

69

850

020

03

701

480

00

260

881

11

900

023

61

796

580

00

196

70

020

020

631

dip

lom

alo

g(em

plo

ymen

t)5

605

530

835

385

551

025

305

330

964

864

871

140

013

000

1lo

g(m

achi

nes

pro

du

ctio

n1

891

831

132

021

771

071

811

701

061

481

440

900

020

460

02w

orke

rs)

w

orkf

orce

mal

e62

87

650

024

36

628

265

00

267

355

64

550

022

23

604

660

00

232

50

030

990

108

Reg

iona

lp

rice

ind

ex1

051

070

081

061

090

081

041

010

091

041

000

110

010

250

963

log(

com

pen

sati

onof

296

294

027

289

287

036

282

284

022

275

278

026

00

180

027

skil

led

)


log(

wag

esof

skill

ed)

265

264

024

260

264

035

255

256

021

250

248

024

00

30

049

log(

com

pen

sati

onof

un

skill

ed)

271

269

031

265

263

036

246

243

025

239

240

027

00

280

07

log(

wag

esof

uns

kille

d)

240

238

028

236

233

034

219

216

024

214

214

024

00

40

099

Rel

ativ

eco

mp

ensa

tion

507

500

151

476

450

123

463

400

131

439

400

139

00

170

175

Lab

ortu

rnov

erra

te5

212

008

623

822

005

237

364

0010

19

744

500

806

00

260

948

log(

tota

lno

em

plo

yees

1999

0

070

110

540

050

110

700

210

190

620

350

240

910

010

870

191

tota

lno

em

plo

yees

1992

)b

log(

tota

lno

em

plo

yees

1999

0

040

030

130

25to

tal

no

emp

loye

es19

92)c

Ou

tof

busi

ness

by19

990

170

000

380

180

000

390

160

000

370

090

000

290

210

920

13

Nu

mbe

rof

pla

nts

incl

ust

er98

7817

395

Sign

ica

nt

at10

Sign

ica

ntat

5

aI

Un

ion

vs

nonu

nio

n

rms

II

Low

-vs

hig

h-in

volv

emen

tu

nio

ncl

ust

ers

III

Low

-vs

hig

h-in

volv

emen

tno

nuni

oncl

ust

ers

bC

ond

itio

nal

onbe

ing

stil

lin

busi

nes

sin

1999

c

Em

plo

ymen

teq

ual

sn

egat

ive

in

nit

yfo

r

rms

that

shu

td

own

thu

son

lyth

em

edia

nis

wel

ld

en

ed


TA

BL

EIV

T

he

Ef

fe

ct

of

th

eS

um

ma

ry

Ind

ex

on

Wa

ge

sa

nd

Tu

rn

ov

er

a

(1)

(2)

(3)

(4)

ln(U

nski

lled

Wag

es)

ln(U

nski

lled

Com

pen

sati

on)

Rel

ativ

eC

ompe

nsa

tion

Turn

over

Rat

e

Var

iabl

eB

SEB

SEB

SEB

SE

(Con

stan

t)0

964

018

01

065

022

61

711

120

625

910

967

1A

vera

geag

e0

001

000

20

012

000

20

033

001

50

213

008

8C

anad

a0

345

003

80

279

004

50

122

025

83

158

206

6

wor

kfor

cew

ith

HS

dip

lom

a0

002

000

10

002

000

10

004

000

40

068

003

5lo

g(em

plo

ymen

t)0

059

001

30

075

001

60

253

008

80

692

059

5lo

g(m

achi

nes

pro

dn

wor

kers

)0

008

001

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007

001

40

068

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31

092

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wor

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001

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20

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iona

lp

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016

80

275

088

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145

625

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002

60

114

003

00

134

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91

155

109

2E

mp

loye

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30

015

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90

111

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607

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plo

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( z)

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001

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069

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287

012

00

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059

7

R2

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016

30

093

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dex

and

its

inte

ract

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wit

hu

nio

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807

234

330

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mp

lesi

zen

318

318

319

318

Sign

ica

nt

at10

Sign

ica

ntat

5

aD

iffe

ren

ces

inu

nion

vs

nonu

nio

nco

ef

cien

tsw

ere

not

sign

ica

nt









BL

EV

E

ffe

cts

on

Wa

ge

sa

(A)

The

Eff

ect

ofSu

bind

ices

(1)

(2)

(3)

(4)

Ind

epen

den

tV

aria

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BSE

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Dep

end

ent

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iabl

e=

log(

Wag

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Uns

kille

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ioni

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shop

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kers

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50

026

012

40

025

009

60

027

008

80

027

Sup

por

tive

HR

pra

ctic

es( z

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nun

ion

001

40

016

000

40

016

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por

tive

HR

pra

ctic

es( z

)u

nion

003

90

012

003

60

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

005

00

013

005

10

014

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

90

018

001

00

021

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

000

00

017

000

80

017

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rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

00

018

005

00

019

R2

050

40

516

050

80

513

F-t

est

on3

subi

nd

ices

inu

nion

wor

kpla

ces

410

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-tes

ton

3su

bin

dic

esin

non

un

ion

wor

kpla

ces

414

6Sa

mp

lesi

zen

319

318

319

318

Dep

end

ent

vari

able

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mp

loye

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er

Un

ioni

zed

shop

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kers

134

21

100

144

51

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115

01

173

111

41

234

Sup

por

tive

HR

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)u

nion

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70

602

017

80

680

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por

tive

HR

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nun

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009

00

543

009

90

529

Dir

ect

emp

loye

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t( z

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nun

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108

30

557

122

70

658

Dir

ect

emp

loye

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volv

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t( z

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nion

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81

201

059

61

417

Rep

rese

ntat

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emp

loye

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707

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40

737

Rep

rese

ntat

ive

emp

loye

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nion

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540

103

20

854


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009

20

100

009

50

169

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mp

lesi

zen

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ffec

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por

tive

HR

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end

ent

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elo

g(W

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nski

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kers

)

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epen

den

tV

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shop

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10

027

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70

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ect

emp

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013

005

10

014

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ect

emp

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90

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20

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ting

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ple

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age

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um

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for

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per

cent

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orce

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ha

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the

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cent

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nion

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not

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ica

nt


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EV

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ects

of

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us

ter

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20

013

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20

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204

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004

20

062

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344

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10

002

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10

006

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40

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80

828

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001

30

003

001

50

052

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118

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cem

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001

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003

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30

021

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lp

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50

405

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625

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038

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642

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8

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910

120

642

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097

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mp

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zen

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319

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ica

nt

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gni

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fere

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ci

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en

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EV

II

Tes

ts

of

Th

eo

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ect

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ple

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ion

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122

012

40

027

011

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024

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20

024

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exof

trai

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( z)

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087

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30

019

000

80

018

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exof

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10

068

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50

010

001

40

010

Dir

ect

emp

loye

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013

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012

Dir

ect

emp

loye

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nion

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001

70

019

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012

00

516

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80

517

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157

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80

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mp

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319

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nts

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hou

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exof

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100

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ntin

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tens

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plo

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end

ent

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elo

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ioni

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shop

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031

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por

tive

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ctic

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nun

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018

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por

tive

HR

pra

ctic

es( z

)u

nion

003

90

019

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

80

016

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ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

70

022

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rese

ntat

ive

emp

loye

ein

volv

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t( z

)no

nun

ion

001

40

018

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rese

ntat

ive

emp

loye

ein

volv

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005

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020

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mm

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tens

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and

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004

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um

my

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ica

ntat

10

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gni

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at5

a

All

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roll

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aver

age

age

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ada

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dir

ect

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ol

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me

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on

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plo

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9

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ival

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ple

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ms

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sine

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mp

leFu

llSa

mp

lein

2000

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DP

DX

SEB

SE

(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

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149

000

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001

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40

174

Em

plo

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t19

93sq

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pri

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dex

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324

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11

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nio

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rs29

640

675

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625

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du

ctio

nw

orke

rs)

298

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155

183

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ect

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ent

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uni

on12

566

510

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275

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210

447

437

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dex

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)no

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on22

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552

953

292

47

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777

54

corr

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assi

ed

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8Sa

mp

lesi

zen

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332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant











































References





































































4 Data and Methods
















































TA

BL

EII

B

as

el

ine

Wa

ge

Eq

uatio

ns

(1)

(2)

(3)

(4)

ln(u

nsk

illed

wag

es)

ln(u

nski

lled

com

pen

sati

on)

Rel

ativ

eC

omp

ensa

tion

Lab

orTu

rnov

erR

ate

BSE

BSE

BSE

BSE

(Con

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References

























































































TA

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References

















































51 Incidence








TA

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EII

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References
















































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References
















































(B)

Clu

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exis

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References
















































log(

wag

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ed)

265

264

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260

264

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256

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248

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pen

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271

269

031

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263

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246

243

025

239

240

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00

280

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kille

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238

028

236

233

034

219

216

024

214

214

024

00

40

099

Rel

ativ

eco

mp

ensa

tion

507

500

151

476

450

123

463

400

131

439

400

139

00

170

175

Lab

ortu

rnov

erra

te5

212

008

623

822

005

237

364

0010

19

744

500

806

00

260

948

log(

tota

lno

em

plo

yees

1999

0

070

110

540

050

110

700

210

190

620

350

240

910

010

870

191

tota

lno

em

plo

yees

1992

)b

log(

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lno

em

plo

yees

1999

0

040

030

130

25to

tal

no

emp

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es19

92)c

Ou

tof

busi

ness

by19

990

170

000

380

180

000

390

160

000

370

090

000

290

210

920

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Nu

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er98

7817

395

Sign

ica

nt

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Sign

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aI

Un

ion

vs

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rms

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Low

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III

Low

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emen

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ust

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ond

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nal

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ing

stil

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busi

nes

sin

1999

c

Em

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ual

sn

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in

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td

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ed


TA

BL

EIV

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he

Ef

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th

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Wa

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References
















































TA

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References























































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References
















































R2

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100

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169

F-t

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subi

nd

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nion

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kpla

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ces

121

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lesi

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309

309

309

309

(B)

Th

eE

ffec

tof

Sup

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tive

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Pra

ctic

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Dep

end

ent

Var

iabl

elo

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ages

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nski

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Wor

kers

)

(1)

(2)

Ind

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den

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aria

ble

BSE

BSE

Un

ioni

zed

shop

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kers

010

10

027

010

70

028

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)no

nun

ion

005

00

013

005

10

014

Dir

ect

emp

loye

ein

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emen

t( z

)u

nion

000

30

020

000

90

021

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rese

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nu

nion

000

90

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001

10

017

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rese

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005

10

020

005

20

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Con

ting

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pay

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on( z

)0

020

001

5C

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037

001

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mp

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on( z

)0

020

001

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mp

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6

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Sam

ple

size

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931

9

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orce

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Can

ada

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per

cent

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(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

20

149

000

20

001

174

40

174

Em

plo

ymen

t19

93sq

uar

ed0

000

000

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00E

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a6

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570

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30

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117

956

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vera

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e4

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502

50

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628

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wor

kfor

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ith

HS

dip

lom

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147

40

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72

427

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1

wor

kfor

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ale

045

11

290

000

60

007

207

61

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egio

nal

pri

cein

dex

348

110

324

540

283

11

692

527

4327

414

3U

nio

nize

dsh

opw

orke

rs29

640

675

340

046

034

654

479

625

12lo

g(m

achi

nes

pro

du

ctio

nw

orke

rs)

298

5432

069

025

90

155

183

427

698

Ind

exof

dir

ect

invo

lvem

ent

( z)

uni

on12

566

510

020

275

023

210

447

437

99In

dex

ofd

irec

tin

volv

emen

t( z

)no

nuni

on22

589

352

710

156

019

552

953

292

47

R2

054

777

54

corr

ectl

ycl

assi

ed

068

8Sa

mp

lesi

zen

128

332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant











































References

























































BL

EV

II

Tes

ts

of

Th

eo

rie

s

(A)

Doe

sTr

ain

ing

Exp

lain

the

Wag

e-In

volv

emen

tR

elat

ion

a

All

Pla

nts

(1)

(2)

(3)

(4)

Dir

ect

EIb

ln(U

nski

lled

Wag

es)

ln(U

nsk

illed

Wag

es)

ln(U

nski

lled

Wag

es)

Sam

ple

BSE

BSE

BSE

BSE

Un

ion

008

90

122

012

40

027

011

80

024

012

20

024

Ind

exof

trai

ning

( z)

non

un

ion

008

40

087

001

30

019

000

80

018

Ind

exof

trai

ning

( z)

un

ion

008

10

068

001

50

010

001

40

010

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

005

00

013

004

90

012

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

90

020

001

70

019

R2

012

00

516

049

80

517

F-t

est

ofth

eeq

ual

ity

ofbo

ldco

ef

cien

ts1

157

656

80

719

353

3Sa

mp

lesi

zen

332

319

319

319

Pla

nts

wit

hou

tR

ecen

tH

irin

g

(5)

(6)

(7)

ln(U

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ille

dW

age)

ln(U

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lled

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e)ln

(Un

skil

led

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e)

Sam

ple

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BSE

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Un

ion

009

90

138

004

50

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007

60

027

Ind

exof

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ning

( z)

non

un

ion

000

30

021

004

00

019

Ind

exof

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ning

( z)

un

ion

005

80

009

005

40

009

Dir

ect

emp

loye

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volv

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t( z

)no

nun

ion

006

60

024

007

10

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Dir

ect

emp

loye

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)u

nion

006

50

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005

10

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R2

061

30

586

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tof

the

equ

alit

yof

bold

coef

ci

ents

136

90

312

081

3Sa

mp

lesi

zen

8686

86


(B)

Ef

cien

cyW

ages

and

Sup

ervi

sion

Turn

over

Turn

over

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(2)

Who

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mp

leB

SEB

SE

Un

ion

044

61

308

088

61

350

z(Tr

ain)

087

90

532

079

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545

z(D

irec

t)0

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060

9ln

(uns

kill

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age)

123

602

860

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010

10

171

Sam

ple

size

n29

529

5

Th

eE

ffec

tof

Sup

ervi

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Doe

sL

oyal

tyM

edia

te

Dir

ect

EIc

ln(U

nsk

ille

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ages

)D

irec

tE

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skil

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Wag

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Who

leSa

mp

leB

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SE

Un

ion

006

90

120

012

40

025

005

50

116

012

50

026

Dir

ect

emp

loye

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t( z

)no

nun

ion

004

60

013

005

20

021

Dir

ect

emp

loye

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emen

t( z

)u

nion

001

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001

70

014

Ind

exof

mgt

re

por

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wor

ker

loya

lty

( z)

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nio

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006

60

009

002

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dex

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orts

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rlo

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nion

034

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100

000

10

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ervi

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w

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r1

168

037

20

211

011

3

F-t

est

onbo

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dic

es5

983

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727

633

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per

viso

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013

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ple

size

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9

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tinu

ed)


TA

BL

EV

II

(co

ntin

ue

d)

(C)

Wag

esan

dIn

tens

ive

Em

plo

yee

Invo

lvem

enta

Dep

end

ent

Var

iabl

elo

g(U

nsk

ille

dW

age)

Ind

epen

den

tV

aria

ble

BSE

Un

ioni

zed

shop

wor

kers

009

940

031

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

000

870

018

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

003

90

019

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

80

016

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

70

022

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

001

40

018

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

40

020

Du

mm

yfo

rin

tens

ive

EI

and

non

uni

on0

038

004

8D

um

my

for

inte

nsiv

eE

Ian

du

nio

n0

033

006

1

R2

053

6F

-sta

tist

icon

incl

usi

onof

du

mm

yva

riab

les

046

1Sa

mp

lesi

zen

319

Sign

ica

ntat

10

Si

gni

cant

at5

a

All

regr

essi

ons

cont

roll

edfo

rw

orkf

orce

aver

age

age

ad

um

my

for

Can

ada

the

per

cent

age

ofth

ew

orkf

orce

wit

ha

hig

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hoo

ld

iplo

ma

log(

emp

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lo

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ach

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p

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uct

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kers

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erce

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eof

the

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kfor

cem

ale

and

the

regi

onal

pri

cein

dex

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant

b

Th

eco

ef

cien

tson

dir

ect

EI

do

not

chan

geby

ast

atis

tica

llysi

gni

can

tam

oun

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hen

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ind

ices

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aini

ngar

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ded

c

Th

eco

ef

cien

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dir

ect

emp

loye

ein

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emen

td

on

otch

ange

bya

stat

isti

cally

sign

ica

ntam

ount

wh

enth

ein

dic

esgi

ftsu

per

visi

onor

oflo

yalt

yar

ead

ded

[com

par

ing

colu

mn

(2)

or(4

)he

rean

dth

eco

rres

pon

din

gre

gres

sion

inTa

ble

V]


























TA

BL

EV

III

Eff

ec

tso

fE

mpl

oy

ee

Inv

ol

ve

me

nt

on

Em

plo

ym

en

tG

ro

wth

1

99

3ndash1

99

9

(1)

(2)

(3)

OL

SE

mpl

oym

ent

1999

OL

SE

mp

loym

ent

1999

Log

itS

urv

ival

Sam

ple

Fil

ms

Stil

lin

Bu

sine

ssFu

llSa

mp

leFu

llSa

mp

lein

2000

BSE

DP

DX

SEB

SE

(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

20

149

000

20

001

174

40

174

Em

plo

ymen

t19

93sq

uar

ed0

000

000

09

00E

076

00E

075

57E

041

92E

04C

anad

a6

824

100

570

121

30

571

108

117

956

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vera

geag

e4

125

502

50

044

002

61

628

471

3

wor

kfor

cew

ith

HS

dip

lom

a2

126

147

40

010

000

72

427

151

1

wor

kfor

cem

ale

045

11

290

000

60

007

207

61

15R

egio

nal

pri

cein

dex

348

110

324

540

283

11

692

527

4327

414

3U

nio

nize

dsh

opw

orke

rs29

640

675

340

046

034

654

479

625

12lo

g(m

achi

nes

pro

du

ctio

nw

orke

rs)

298

5432

069

025

90

155

183

427

698

Ind

exof

dir

ect

invo

lvem

ent

( z)

uni

on12

566

510

020

275

023

210

447

437

99In

dex

ofd

irec

tin

volv

emen

t( z

)no

nuni

on22

589

352

710

156

019

552

953

292

47

R2

054

777

54

corr

ectl

ycl

assi

ed

068

8Sa

mp

lesi

zen

128

332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant











































References
















































(B)

Ef

cien

cyW

ages

and

Sup

ervi

sion

Turn

over

Turn

over

(1)

(2)

Who

leSa

mp

leB

SEB

SE

Un

ion

044

61

308

088

61

350

z(Tr

ain)

087

90

532

079

20

545

z(D

irec

t)0

019

060

9ln

(uns

kill

edw

age)

123

602

860

R2

010

10

171

Sam

ple

size

n29

529

5

Th

eE

ffec

tof

Sup

ervi

sion

Doe

sL

oyal

tyM

edia

te

Dir

ect

EIc

ln(U

nsk

ille

dW

ages

)D

irec

tE

Icln

(Un

skil

led

Wag

es)

Who

leSa

mp

leB

SEB

SEB

SEB

SE

Un

ion

006

90

120

012

40

025

005

50

116

012

50

026

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

60

013

005

20

021

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

001

50

018

001

70

014

Ind

exof

mgt

re

por

tsof

wor

ker

loya

lty

( z)

nonu

nio

n0

420

006

60

009

002

4In

dex

ofm

gt

rep

orts

ofw

orke

rlo

yalt

y( z

)u

nion

034

60

100

000

10

017

Sup

ervi

sors

w

orke

r1

168

037

20

211

011

3

F-t

est

onbo

thlo

yalt

yin

dic

es5

983

461

727

633

048

4(a

ndsu

per

viso

rsh

opw

orke

r)

R2

013

20

525

024

50

516

Sam

ple

size

n33

031

733

231

9

(con

tinu

ed)


TA

BL

EV

II

(co

ntin

ue

d)

(C)

Wag

esan

dIn

tens

ive

Em

plo

yee

Invo

lvem

enta

Dep

end

ent

Var

iabl

elo

g(U

nsk

ille

dW

age)

Ind

epen

den

tV

aria

ble

BSE

Un

ioni

zed

shop

wor

kers

009

940

031

Sup

por

tive

HR

pra

ctic

es( z

)no

nun

ion

000

870

018

Sup

por

tive

HR

pra

ctic

es( z

)u

nion

003

90

019

Dir

ect

emp

loye

ein

volv

emen

t( z

)no

nun

ion

004

80

016

Dir

ect

emp

loye

ein

volv

emen

t( z

)u

nion

000

70

022

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)no

nun

ion

001

40

018

Rep

rese

ntat

ive

emp

loye

ein

volv

emen

t( z

)u

nion

005

40

020

Du

mm

yfo

rin

tens

ive

EI

and

non

uni

on0

038

004

8D

um

my

for

inte

nsiv

eE

Ian

du

nio

n0

033

006

1

R2

053

6F

-sta

tist

icon

incl

usi

onof

du

mm

yva

riab

les

046

1Sa

mp

lesi

zen

319

Sign

ica

ntat

10

Si

gni

cant

at5

a

All

regr

essi

ons

cont

roll

edfo

rw

orkf

orce

aver

age

age

ad

um

my

for

Can

ada

the

per

cent

age

ofth

ew

orkf

orce

wit

ha

hig

h-sc

hoo

ld

iplo

ma

log(

emp

loym

ent)

lo

g(m

ach

ines

p

rod

uct

ion

wor

kers

)th

ep

erce

ntag

eof

the

wor

kfor

cem

ale

and

the

regi

onal

pri

cein

dex

Dif

fere

nce

sin

uni

onvs

no

nun

ion

coef

ci

ents

wer

en

otsi

gni

cant

b

Th

eco

ef

cien

tson

dir

ect

EI

do

not

chan

geby

ast

atis

tica

llysi

gni

can

tam

oun

tw

hen

the

ind

ices

oftr

aini

ngar

ead

ded

c

Th

eco

ef

cien

tson

dir

ect

emp

loye

ein

volv

emen

td

on

otch

ange

bya

stat

isti

cally

sign

ica

ntam

ount

wh

enth

ein

dic

esgi

ftsu

per

visi

onor

oflo

yalt

yar

ead

ded

[com

par

ing

colu

mn

(2)

or(4

)he

rean

dth

eco

rres

pon

din

gre

gres

sion

inTa

ble

V]


























TA

BL

EV

III

Eff

ec

tso

fE

mpl

oy

ee

Inv

ol

ve

me

nt

on

Em

plo

ym

en

tG

ro

wth

1

99

3ndash1

99

9

(1)

(2)

(3)

OL

SE

mpl

oym

ent

1999

OL

SE

mp

loym

ent

1999

Log

itS

urv

ival

Sam

ple

Fil

ms

Stil

lin

Bu

sine

ssFu

llSa

mp

leFu

llSa

mp

lein

2000

BSE

DP

DX

SEB

SE

(Con

stan

t)33

465

945

414

55

676

224

417

023

837

532

9E

mp

loym

ent

1993

068

20

149

000

20

001

174

40

174

Em

plo

ymen

t19

93sq

uar

ed0

000

000

09

00E

076

00E

075

57E

041

92E

04C

anad

a6

824

100

570

121

30

571

108

117

956

9A

vera

geag

e4

125

502

50

044

002

61

628

471

3

wor

kfor

cew

ith

HS

dip

lom

a2

126

147

40

010

000

72

427

151

1

wor

kfor

cem

ale

045

11

290

000

60

007

207

61

15R

egio

nal

pri

cein

dex

348

110

324

540

283

11

692

527

4327

414

3U

nio

nize

dsh

opw

orke

rs29

640

675

340

046

034

654

479

625

12lo

g(m

achi

nes

pro

du

ctio

nw

orke

rs)

298

5432

069

025

90

155

183

427

698

Ind

exof

dir

ect

invo

lvem

ent

( z)

uni

on12

566

510

020

275

023

210

447

437

99In

dex

ofd

irec

tin

volv

emen

t( z

)no

nuni

on22

589

352

710

156

019

552

953

292

47

R2

054

777

54

corr

ectl

ycl

assi

ed

068

8Sa

mp

lesi

zen

128

332

82

Sign

ica

nt

at10

Sign

ica

ntat

5

Not

eIn

dep

end

ent

vari

able

sar

efr

omth

esa

me

1993

surv

eyas

inth

eot

her

tabl

esD

iffe

ren

ces

inu

nion

vsn

onu

nion

coef

ci

ents

wer

en

otsi

gni

cant











































References
















































TA

BL

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Employee Involvement and Pay at US and Canadian Auto...

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