does deferred compensation increase worker effort?

DOES DEFERRED COMPENSATION INCREASEWORKER EFFORT?*manc_2157 381..404

bySCOTT J. ADAMS

University of Wisconsin-Milwaukeeand

JOHN S. HEYWOOD†

University of Wisconsin-Milwaukee and Birmingham Business School,University of Birmingham

This paper presents a model illustrating that deferred compensationincreases effort (reduces shirking) by increasing the cost of job loss.Importantly, the size of this increase in effort shrinks as the chance ofexogenous job separation grows. The paper tests the model’s predictionsusing both US and Australian data. In both countries we find empiricalresults consistent with the model’s predictions. Deferred compensation,as identified either by pensions or by steeper tenure–wage profiles, isassociated with greater self-reported worker effort. Moreover, when theprobability of job separation is greater, the influence of deferredcompensation diminishes.

1 Introduction

The view that firms strategically defer compensation has enjoyed currency forat least a quarter of a century. In the most common version of this view, firmsuse deferred compensation to increase the cost of job loss and this, in turn,causes workers to increase their effort. While there has been empirical testingbroadly associated with the contention that firms strategically defer compen-sation (as reviewed in the next section), the link between deferred compen-sation and effort deserves more direct scrutiny. In particular, we present thefirst systematic estimates of the association between self-reported measures ofworker effort and deferred compensation.

In the next section we review both past theories and evidence on thestrategic deferral of compensation. We show that at least four strands oftesting exist but that none directly estimates the relationship betweendeferred compensation and worker effort. The third section presents anillustrative model of the relationship between effort, deferred compensationand other elements of the labor market. We derive a series of comparativestatic results that form the core of our empirical testing. In the fourthsection we review our data from the USA, the National Study of the

* Manuscript received 28.7.08; final version received 24.6.09.† The authors thank Uwe Jirjahn, Knut Gerlach, Glen Waddell, two reviewers and seminar

participants at the University of Naples, University of Oregon and the University ofHanover.

The Manchester School Vol 79 No. 3 381–404 June 2011doi: 10.1111/j.1467-9957.2009.02157.x

© 2010 The AuthorsThe Manchester School © 2010 Blackwell Publishing Ltd and The University of ManchesterPublished by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK, and 350 Main Street, Malden, MA 02148, USA.

381

Changing Workforce (NSCW), and from Australia, the Australian Work-place Industrial Relations Survey (AWIRS). The results of tests using thesedata are summarized in the fifth section, where we report a robust positiverelationship between effort and deferred compensation. Moreover, wepresent evidence that the effect of deferred compensation on effort shrinksas the probability of exogenous separation increases. These results are con-sistent both across countries and with the predictions of our model, sug-gesting that workers respond as predicted by the literature on strategicallydeferred compensation. The sixth section concludes, presents caveats andsuggests further research.

2 Theory and Evidence on the Role of Deferred Compensation

The most common theory of deferred compensation assumes employers canprofitably rearrange the tenure–earnings profile to increase worker effort(Lazear, 1979, 1981). Workers are paid less than their productivity early intheir tenure and accumulate quasi-rents paid back late in their tenure.Thus, immediately after starting, a wedge emerges between a worker’sexpected value of lifetime compensation in the current job and that in anyalternative job. As the extent of deferral increases, workers reduce shirking(increase effort) because the cost of losing the current job increases. Thisincrease in productivity allows overall compensation to be higher andensures participation of workers in the scheme of deferred compensation.This theory can be contrasted with the notion that deferred compensationis used to attract workers with longer expected tenure (Salop and Salop,1976), with the contention that workers may simply prefer increasing wageseven with the same or lower expected value (Lowenstein and Sicherman,1991) and with the view that returns to training drive deferred compensa-tion (Levine, 1993).

The evidence on theories of deferred compensation consists of severalstrands. Medoff and Abraham (1980, 1981) examined a large US firm arguingthat the strong seniority-wage profile was largely independent of reportedperformance measures. This finding has been confirmed for other large firms,in other countries and with alternative performance measures (Flabbi andIchino, 2001; Dohmen, 2004) and has been taken as evidence consistent withtheories of deferred compensation (Flabbi and Ichino, 2001, p. 385).

The second strand of evidence directly estimates the return to firm-specific seniority independent of experience and other indicators of workerproductivity. The size of this return is highly disputed (contrast Altonji andShakotko (1987) and Abraham and Farber (1987) with Topel (1991)). Morerecently, Buchinsky et al. (2002) estimate a multi-equation system endogeniz-ing mobility and participation and present a 10-year increment of 40 per cent.Altonji and Williams (2005), however, emphasize the importance of generallabor market experience and suggest that 10 years of seniority increase wages

The Manchester School382

© 2010 The AuthorsThe Manchester School © 2010 Blackwell Publishing Ltd and The University of Manchester

by 10 per cent. Despite the controversy, Dohmen (2004, p. 741) concludesthat ‘even estimates near the bottom of the range are large enough to justifyan inquiry into the causes of such wage growth’. Yet, as is well recognized,strategic rearrangement of the earnings profile is only one of these possiblecauses of such wage growth.

A third strand of evidence compares the tenure–earnings profile acrossrelevant groups of workers. Lazear and Moore (1984) compare independentcontractors, who have no incentive to rearrange their compensation, withemployees doing identical jobs. The wage profile of the employees emerges asmuch steeper. Kotlikoff and Gokhale (1992) compare sales workers withmore nearly verifiable productivity and their managers with less clearly veri-fiable productivity. The earnings profiles of the managers show greaterdeferred compensation with earnings below productivity early in the careerand above productivity late in their career. The sales workers show essentiallyno difference between earnings and productivity at any point over theircareers.

The fourth strand of evidence presents correlations consistent with thebroader shirking model underlying the theory of deferred compensation.Groshen and Krueger (1990) confirm that the slope of the earnings profilevaries inversely with the intensity of monitoring. This they suggest indicatesthat the increased productivity that can be gained through monitoring canalso be gained from deferred compensation. Heywood and Wei (1997) useestablishment-level data and Barth (1997) uses individual data to confirm thetrade-off between the slope of the earnings profile and the use of individualperformance pay such as piece rates. When productivity can be directlyverified, the use of deferred compensation is unnecessary and profiles areflatter.

In the current paper, we present a different approach from thosereviewed. We test a critical portion of the theory of deferred compensationwith self-reported measures of worker effort. The basic prerequisite for thefirms to profitably use deferred compensation is that the effort of workersincreases as a result. We test this and other implications about worker behav-ior derived from a representative model. We do not examine firm decisions touse deferred compensation but instead concentrate on testing for the workerresponses necessary for such a decision to be profitable.1

3 Deferred Compensation and Effort

We now illustrate that deferring compensation increases the cost of jobloss and that this increases work effort (Lazear, 1979, 1981). Following

1Nonetheless, we note that Orazem et al. (2004) have shown that firms with workers character-ized by long-term attachment have higher rates of return on assets. While not the onlyexplanation, both long-term attachment and greater effort (productivity) may follow fromdeferred compensation.

Deferred Compensation and Worker Effort 383


convention, workers face a dichotomous effort decision (Malcomson, 1981).During any period, the worker either works or shirks, resulting in a frequencyof work effort e (0 < e < 1) or of shirking (1 - e). The firm responds to shirkingby introducing incentives. We define m as monitoring intensity and assumethat the probability of catching a worker shirking is a positive function c(m),where 0 < c < 1, c(0) = 0 and c′(m) > 0. (Primes indicate derivatives andsubscripts indicate partial derivatives.) If caught shirking, the worker is fired,so the probability of firing is (1 - e)c(m).

We assume a two-period model ignoring discounting as it does notqualitatively alter the model. We imagine potential earnings over the twoperiods equal to w1 + w, where w1 is the value of wages earned during theaccumulation period and w < w1 is the fall back value of market wages earnedduring the reward period during which deferred compensation no longerserves as an incentive. We assume the total earnings of the two periods aresufficient that under the equilibrium conditions the representative worker atleast matches the reservation utility of not participating in the deferred com-pensation scheme.2 The actual payment of wages in the first period is (1 -d)w1, and the payment in the second period is dw1 + w, where 0 < d < 1measures the extent of deferred earnings. A hired worker is guaranteed thefirst period payment and is only concerned with next period income thatdepends on not being fired for shirking. Thus, the worker maximizes expectedutility over this period’s effort and next period’s income.

Consider a representative worker with a von Neumann–Morgensternutility function with standard assumptions of risk aversion and increasingdisutility of effort:

U E S Y V e S S V V= ( )[ ] − ( ) ′ > ′′ < ′ > ′′ >0 0 0 0, ; , (1)

where E is the expectations operator, S is the utility of income Y, and V is thedisutility of effort.

We develop the first term in equation (1) recognizing that expectedincome depends on more than simply the hiring and monitoring decisions ofthe firm. We note that the worker may separate from the firm for reasonsunrelated to the worker’s effort choice with known probability b < 1. Thus, adownturn might force the layoff of a worker not shirking. Alternatively, theworker may intend to move or seek an alternative job. Second, householdincome consists not only of the labor earnings of the respondent but of thelabor income of the spouse and nonlabor income, h. Third, the conditions ofthe labor market influence whether or not the worker can easily obtainanother job and hence face a smaller loss of income from layoff or firing. Weidentify f > 0 as the probability of immediate reemployment conditional upon

2We also assume that the firm is at least as profitable using deferred compensation as not usingdeferred compensation, but we emphasize that we are not examining the firm’s behavior butonly that of the worker.



job loss and assume that if reemployed the worker earns amount w.3 Thisdevelopment allows us to identify expected income:

E S Y b e c m S w w hb e c m b fS w

( )[ ] = −( ) − −( ) ( )[ ] + +( )+ −( ) −( ) ( ) +[ ]1 1 1

1 11δ

++( ) + −( ) ( )[ ]h f S h1 (2)

The first set of terms is the expected value of retention, and the second is theexpected value of not being retained, the product of the probability of eitherfiring or layoff and the expected value of finding or not finding a job for thesecond period.

Substituting equation (2) into equation (1) and maximizing with respectto effort yields the first-order condition for an interior effort equilibrium:

11 0

1−( ) ( ) + +( ) − +( )[{+ −( ) ( )]} − ′ ( ) =

b c m S w w h fS w hf S h V e

δ(3)

The second-order condition Uee = -V″(e) is negative by construction.The choice of e can be viewed as a function of the relevant independent

variables, and applying the implicit function theorem derives the comparativestatics:

e e m h f b

e b c m S w w h w V e

e bm

* = ( )

= −( ) ( ) ′ + +( ) ′′ ( ) >

= −( )

δ

δδ

, , , ,

*

*

1 0

1

1 1

′′ ( ) + +( ) − +( ) + −( ) ( )[ ]{ } ′′ ( ) >

= − ( )

c m S w w h fS w h f S h V e

e c m S wb

δ

δ

1 1 0

* 11 1 0

1

+ +( ) − +( ) + −( ) ( )[ ]{ } ′′ ( ) <

= − −( ) ( ) +( )

w h fS w h f S h V e

e b c m S w hf* −− ( )[ ] ′′ ( ) <

= −( ) ( ) ′ + +( ) − ′ +( ) + −( ) ′

S h V e

e b c m S w w h fS w h f S hh

0

1 11* δ (( )[ ]{ } ′′ ( ) <V e 0

(4)

Increasing the share of compensation deferred increases the cost of job loss.In turn, this increases worker effort. This is the central hypothesis of thetheory of deferred compensation. Increasing monitoring increases the chanceof job loss and so its expected value. Increasing the separation probabilityreduces the cost of job loss and causes worker effort to decline. Similarly,a higher chance of reemployment reduces the cost of job loss. Finally, anincrease in spouse’s income reduces the utility decline associated with job losscausing a decrease in effort.

As an important second-order prediction, the influence of deferredcompensation in increasing effort varies negatively with the probability ofseparation:

3The only requirement for the model’s comparative statics is that the amount earned uponreemployment is less than that which would be earned in the second period with the currentemployer.



e c m S w w h w V ebδ δ,* = − ( ) ′ + +( ) ′′ ( ) <1 0 (5)

Thus, if a worker knew with certainty that he or she will separate from thefirm, deferred compensation plays no role in motivating current effort. Simi-larly, the strength of that motivation will be largest if the worker knows thatthe only reason he or she would not be employed by the firm next period is ifthey are caught shirking (b = 0).

Finally, by construction, deferred compensation can have no influenceon effort in the second period, the reward period. During this period, retainedworkers are repaid the compensation deferred from the first period. In con-tinuous time, the ‘terminal period’ problem would be that once the worker isassured of being repaid the deferred compensation (a pension is vested,partnership is attained or tenure is granted), deferred compensation stopsmotivating effort. Similarly, if deferred compensation is repaid graduallythroughout the later years of tenure (instead of as a lump sum), its ability tomotivate effort declines as tenure increases.

This illustrative model presents a set of comparative statics not previ-ously estimated and not typically implied by other theories. Thus, Salop andSalop (1976) argue that firms may delay compensation to attract workers lesslikely to quit but they do not posit an influence on effort. Similarly, theappearance of deferred compensation can reflect the training investmentrequired by a job and strategically increasing the returns might reduce workerturnover and so protect the investment (Levine, 1993). Deferred compensa-tion may be a response to difficult financial times for a firm or the result ofbargaining when workers have a lower discount rate than managers. Tour-naments may elicit effort and indirectly generate the appearance of deferredcompensation as winners are chosen later in tenure (Lazear and Rosen, 1981).When appropriate, we will attempt to control for alternative theories ofcausation as suggested by these and other theories.

4 Data and Methodology

Our use of data from two countries stands as an important contribution.First, the ability to confirm theoretical predictions across two countries addsto the confidence in the results. Second, Hamermesh (2002) calls for com-parative examinations when the institutions allow somewhat different tests ofthe same theoretical prediction. As we argue below, the availability of pen-sions in the USA is a strong indicator of deferred compensation, but thisindicator is not available in the Australian data. Moreover, the Australianinstitutions suggest that pension availability may not be a strong indicator inany event. Australia’s system of mandated occupational pensions for all butthe lowest wage earners, combined with a government program for thoseworkers, implies that more nearly all workers have a pension (Rein andTurner, 2001). Yet, the matched employer/employee data from Australia



allows us to generate a tenure–earnings profile that is simply unavailable inindividual-level US data. Thus, examining two countries allows a morerobust and detailed test of the theoretical predictions.

4.1 Measuring Effort in the NSCW (USA) and AWIRS (Australia)

Our data are taken from the 1997 NSCW and the 1995 AWIRS. The NSCW,a representative sample of the US civilian labor force, has unusually detailedinformation on the conditions of employment (see National Study of theChanging Workforce, 1999). Individual workers are the unit of observationand answer all questions. After removing the self-employed and observationswith missing data we have a sample size of 2057. The workers are asked torespond to the statement ‘My job requires that I work very hard’. If theystrongly agree with the statement, we categorize them as reporting higheffort. If they do not, we consider them as putting forth low effort.4

The AWIRS data come from two related surveys. The first follows fromquestionnaires administered to managers at establishments that employ 20 ormore workers and includes information on 2001 establishments. Topicscovered include the size of the workforce, the distribution of workers acrossage groups, tenure levels, compensation and benefits, information on howworkers are evaluated, information on recent dismissals and other items. Thesecond survey follows from a questionnaire administered to a random sampleof 19,000 workers from the above set of establishments. It elicits informationon demographics, tenure with the firm, training received, compensation andthe type of work performed. After matching the establishment and workerinformation and removing observations for which there exists insufficientinformation, we use a sample of 11,961 workers.

The measure of effort in the AWIRS asks workers to react to the state-ment ‘I put a lot of effort into my job’. If they strongly agree, we code theseworkers as reporting high effort.5 A higher percentage of workers report higheffort in the Australian data than in the US data (89.0 per cent vs. 68.8 percent). This may flow from differences in the Likert scale or from differencesin the wording of the effort questions. The NSCW asks about what the jobrequires, but the AWIRS asks about the effort expended without mention ofjob requirements.

As we are interested in how effort responds to the cost of job loss, the USmeasure might be less satisfactory. The effort requirements of the job can bedetermined by many factors beyond the cost of job loss such as the pace of anassembly line or the extent of scrutiny by supervisors. Ideally one would like

4The other possible responses are agree, disagree or strongly disagree. Using alternative defini-tions of the binary indicator (such as recoding both strongly agree and agree to one) orestimating ordered probits cause no remarkable changes in the key coefficients or conclu-sions drawn by the paper.

5The Likert scale choices in the AWIRS are not the same as in the NSCW, as the choices areagree, neither agree nor disagree and disagree.



an effort measure that holds constant the job requirements. In the clearabsence of such a measure, we try to control for as many effort determinantsas possible. In addition we will describe robustness checks that make use of asupplementary effort question in the US data and that limit the sample tothose thought to have more choice over effort (e.g. effort is not driven by anassembly line). While no single self-reported effort question is perfect, our useof several that tend to show the same pattern provides some reassurance.

More generally, some may question the use of any self-reported effortmeasure as simply subjective and unreliable. First, effort measures eitheridentical or very similar to ours have been used to test efficiency wage models(Fairris and Alston, 1994) and models of performance pay (Drago andGarvey, 1998), but they have not been used to test theories of deferredcompensation.6 Second, while subjective, they have been shown to provide areasonable picture of the effort that individuals put forth in their current job.Green (2004) uses a question from British surveys almost identical to thatfrom the USA showing that it is very highly correlated with measures of workstress, with objective measures of productivity and with managers’ evalua-tions of worker effort. Third, earlier work by Green and McIntosh (1998)summarizes research by psychologists justifying the use of subjective effortmeasures. This experimental research (see for example Johnson et al., 1995)routinely shows a tight correlation between the laboratory measures of physi-cal and mental effort and individual subjective evaluations of effort.

Thus, following Green (2001, 2004), Drago and Garvey (1998), Greenand McIntosh (1998, 2001) and Fairris and Alston (1994), we use the self-reported effort measure as our dependent variable and estimate its determi-nants. The estimations will be fit to a cumulative normal distribution using theprobit technique. In all estimations we present the marginal effects of theindependent variables on the probability (measured between 0 and 100) ofputting forth high effort. We recognize that some workers may incorrectlyreport working hard and that others may incorrectly report not working hard.Yet, unless these reporting errors are correlated with the measures of deferredcompensation, they will simply bias our estimations toward insignificance.

4.2 NSCW Measures of Effort Determinants

In the NSCW, pension offerings serve as our proxy for deferred compensa-tion. In the USA, pensions may serve as a valuable tool in the strategicdeferral of compensation to workers. First, many of the benefits of employer-provided pensions are clearly deferred due to vesting and the nature ofdefined benefit formulae (Lazear, 1990). Second, pensions have been cited asthe most credible form of deferred compensation as the pension contract bestprevents opportunistic employer behavior (Dorsey et al., 1998, p. 44). Third,

6We also note the huge literature by economists using subjective measures of well-being asdependent variables. See Kahneman and Krueger (2006) for a recent review.



Ippolito (1991) suggests pensions are particularly effective as deferred com-pensation because they increase job tenure more than does increasing theslope of the wage profile.

More generally, if firms wish to defer compensation for strategic reasons,they must find a mechanism to terminate employment for those workers withvery long tenure who have been paid back their quasi-rents. Pensions servethis function. Importantly, as Lazear (1990) makes clear, this can be accom-plished with defined contribution plans as well as defined benefit plans.Ippolito (2002) goes further arguing that defined contribution plans canincrease the effort of a firm’s workforce by screening out low effort workerswith high discount rates. Thus, apart from their actual role in deferringcompensation, employer-provided pensions may signal both the existence ofother deferred compensation and the type of employees the firm desires. Asshown in Table 1, workers reporting high effort are more likely to haveemployer-provided pensions.

Table 1Sample Variables from NSCW (USA)

Overall High effort Low effort

Job is high effort 58.67Demographic controls

Age 39.43 38.37 40.92Female 46.36 50.02 41.17Married 64.80 65.47 63.84Children in household 47.18 48.70 45.02Non-white 19.08 18.13 20.43High school graduate 62.33 59.64 66.15College graduate 32.37 35.16 28.41

Job controlsTenure 7.45 7.12 7.91Hourly earnings 18.97 20.71 16.50Chance for advancement high 25.43 27.69 22.21Full-time 87.09 88.25 85.44Union 15.55 15.12 16.16Training 63.39 64.23 62.20Large establishment 18.37 18.95 17.54Public sector establishment 16.91 15.93 18.31

Deferred compensation (d)Pensions 75.78 77.99 72.65

Monitoring controlOne immediate supervisor (m) 90.04 90.80 88.95

Likelihood of leaving firm in near future (b)Not strongly loyal to employer 26.97 25.01 29.75Frequent layoffs at current employer 11.39 11.80 10.80

Other model variablesEasy to find new job ( f) 61.80 62.74 60.47Other income (h) (family income - one’s own income) 18,223 20,009 15,687

Sample size 2,057 1,209 848

Notes: All estimates are weighted using NSCW sampling weights.



The other variables from the NSCW are reported in the remainder ofTable 1. The measure of supervision (m) is whether or not the respondentcan identify a single immediate supervisor to whom they report. We assumethat such a respondent is more closely monitored than one who cannotidentify an immediate supervisor or who identifies many. As Table 1 shows,about one in 10 workers in our sample could not identify a single imme-diate supervisor.

The variables that proxy the probability of separation, b, are whether theemployer makes frequent use of layoffs and whether the employee expressesloyalty to the employer. Recall that b represents the worker’s perception thathe or she will be separated for reasons other than being caught shirking. Iflayoffs are of broad scale and differ from firing individual workers for shirk-ing, the layoff indicator could be a reasonable fit to the theory and should beexpected to have a negative influence on effort. On the other hand, if theemployer uses performance as a guide for whom to layoff, frequent layoffsmay suggest those with low effort will more likely be fired suggesting apositive influence of layoffs on effort. In the end, which influence dominateswill be settled empirically. The second indicator follows from the notion thatworkers anticipating separation are likely to lack loyalty. Certainly, themanagement literature finds a close link between job insecurity and lowercommitment or loyalty (see Ashford et al., 1989; Iverson 1996). Eitherworkers anticipate leaving of their own accord (highly correlated with lowloyalty) or they anticipate their employer will cause a separation despite theireffort provision (also generating low loyalty). In terms of the model, workersreporting low loyalty will be less likely to be employed by the same firm in thesecond period, a high value of b.

The reemployment probability, f, is proxied by the workers’ perceptionsof whether or not it would be easy to find a new job similar to their currentjob. We calculate the household income other than the labor earnings of therespondent, h, as the difference between the household income and therespondent’s income. At minimum, this includes the income of the respon-dent’s spouse and when this is higher, the respondent is more likely to shirk.One countervailing tendency may be the assortative matching of spouses, inwhich those that work hard and earn more are more likely to be married toeach other. If this dominates, a negative association between spouse’s incomeand respondent’s shirking may emerge.

Recognizing the importance of the cost of job loss highlights the essen-tial similarity between models of deferred compensation and of efficiencywages. While deferring wages would seem to be a cheaper manner to increasethe cost of job loss, there may be circumstances in which the cost of job losscan be profitably increased by providing a current wage above the equilib-rium (Fairris and Alston, 1994). In an effort to distinguish such circum-stances from the use of deferred compensation, we will hold earningsconstant in our preferred estimations.



We also recognize that pensions and other forms of deferred compensa-tion may be a part of tournament payment schemes designed to elicit effortfrom workers as they compete for promotions and higher earnings (Lazearand Rosen, 1981). To isolate the effect of our pension variable as deferredcompensation and not as a tournament reward, we will ultimately control forthe respondent’s perceived chance of advancement in the organization.

While these variables summarize the basic proxies for the theoreticalmodel, a variety of other controls are introduced, which we hypothesizemight independently be correlated with shirking. These include standarddemographic variables such as age, education, gender and racial status. Theyalso include job control variables such as employer size, union status, years oftenure, occupational controls and full-time status. The full list is presented inTable 1.

4.3 AWIRS Measures of Effort Determinants

In the AWIRS, we use the slope of the tenure–earnings profile at a worker’sestablishment as a proxy for deferred compensation (d). We construct thismeasure by dividing the weekly wages of those with over five years of tenureby the weekly wages of those with five or fewer years. The mean level oftenure across establishments is between five and six years, and this proxy hasperformed as expected in work examining the determinants of hiring olderworkers in Australia (Adams and Heywood, 2007). We do not have enoughworkers at each firm to accurately measure slopes of the tenure–wage profileacross the entire tenure distribution, but we will discuss other proxies withwhich we experimented. Table 2 shows that workers reporting high effort areat firms with larger ‘tenure–wage ratios’.

The monitoring proxy (m) indicates the presence of a formal appraisalscheme. Such schemes are increasingly a monitoring tool designed to measureperformance and effort (see Brown and Heywood, 2005). Table 2 indicatesthat workers reporting high effort are more likely to be in workplaces withformal evaluations. We use the AWIRS question on whether a respondentfeels insecure about his future at the job as our proxy for the chance ofexogenous job separation (b). Table 2 indicates that workers insecure abouttheir future report lower effort. The AWIRS provides a question on whetherthere have been recent layoffs at the workplace as a measure of the probabil-ity of separation. These measures differ somewhat from the NSCW measuresthat ask about frequent layoffs at the firm and loyalty.

As for the remaining model concepts of reemployment probability ( f )and other income (h), no acceptable measures exist in the AWIRS. Lookingat Table 1, however, these two concepts do not noticeably differ by effort inthe NSCW. Further analyses in the paper will indicate this as well. Given thewealth of control variables and the many other similarities in the data, theabsence of these variables does not disrupt a reasonable test of the otherpredictions.



As for the remaining control variables, we measure the demographic andjob characteristics as similarly as possible across the two data sets but differ-ences remain. First, the AWIRS contains no marital status variable. Second,we replace the non-white variable from the NSCW with the non-Australianvariable in the AWIRS. Third, earnings are measured weekly in the AWIRS,as opposed to hourly in the NSCW. Finally, the wording of the chance foradvancement variable is different as it asks respondents to rate their chancefor a ‘more senior position’. A quick look at the means of the controlvariables across Tables 1 and 2 reveals that our AWIRS and NSCW samplesare broadly similar.

Our basic methodology for both data sources builds up the set ofexplanatory variables from those directly derived from the theoretical modelto those plus all of the controls. We will be interested in how closely theempirical results match the theoretical predictions with particular attentionpaid to those predictions regarding deferred compensation. Specifically, wetest whether or not deferred compensation routinely associates with greatereffort and whether the size of this association decreases as the exogenousprobability of job separation increases.

Table 2Sample Variables from AWIRS (Australia)

Overall High effort Low effort

Job is high effort 89.4Demographic controls

Age 39.32 39.59 37.04Female 44.73 46.18 32.49Children in household 53.97 53.38 58.96Non-Australian 25.64 25.53 26.62High school graduate 41.01 40.30 47.02College graduate 30.72 31.25 26.18

Job controlsTenure 5.88 5.92 5.52Weekly earnings 628.96 630.85 613.00Chance for a more senior position high 19.93 20.94 11.39Full-time 80.24 79.65 85.23Union 50.21 49.59 55.45Training 61.61 62.11 57.33Large establishment 18.23 18.08 19.43Public sector 13.32 12.98 16.17

Deferred compensation (d)Tenure–wage ratio 1.0227 1.0231 1.0190

Monitoring controlFormal evaluations (m) 74.15 74.48 71.42

Likelihood of leaving firm in near future (b)Insecure about future at job 29.04 28.04 37.42Recent dismissals at workplace 40.30 39.90 43.68

Sample size 11,961 10,649 1312

Notes: All estimates are weighted using AWIRS sampling weights.



5 Results

5.1 Determinants of Effort

We look first at estimates from the USA obtained from the NSCW. InColumn (1) of Table 3, we present our probit estimating worker effort as afunction of the six measures that proxy the theoretical model. This parsimo-nious specification indicates that when compensation is deferred via pensions,respondents are more than 7 percentage points more likely to report higheffort. Given that 59 per cent of workers report high effort, this statisticallysignificant effect is a very large marginal influence. The indicator of closersupervision is associated with an increase of over 4 percentage points in thechance of reporting high effort but falls short of significance at the 0.10 level.The indicator of layoffs emerges as a positive determinant of effort as well butit too falls just short of statistical significance. As suggested, this mightindicate that firms having layoffs are able to target those with low produc-tivity (shirking). The indicator of lack of loyalty, however, is significant andreduces effort by nearly 6 percentage points, suggesting this measure may bea more reliable proxy for the probability of separation. The indicator of ahigh reemployment probability takes a small but insignificant positive sign.The coefficient will shrink further as controls are added. Finally, the indicatorfor other household income (spouse’s income) is positively but not signifi-cantly correlated with effort. Thus, it appears as if there is some evidence ofassortative matching of spouses with regard to effort. In total, several of thebasic hypotheses are supported by the parsimonious estimation. Most impor-tantly, there exists a large positive influence of deferred compensation oneffort.

This influence remains as additional controls are added. Column (2)retains the variables of the theoretical model and adds the demographiccontrols. These controls indicate that age plays a negative and significant rolein self-reported effort. Women are more likely to report high effort, butmarital status and whether there are children in the household do not influ-ence effort significantly. Non-whites are less likely to report high effort. Thereis little evidence of a strong correlation between educational attainment andeffort. Many, but not all, of these results might be anticipated if one recog-nizes that most of these variables are wage determinants. Thus, for instance,non-whites are recognized to earn consistently less than otherwise equalwhites. This yields a lower cost of job loss for non-whites and may, as aconsequence, result in a lower probability of high effort. Crucially, thepattern of results on the indicators that proxy the theoretical model remainslargely unchanged.

Column (3) reports a specification that retains all the variables ofColumn (2) but adds the basic job controls from Table 1. These controls alsoinclude a series of dummy variables indicating major occupation. The pattern



Ta

ble

3D

eter

min

an

ts

of

Wo

rk

erE

ffo

rt

,Ma

rg

ina

lE

ffec

ts

fro

ma

Pr

ob

itM

od

el,N

SCW

(USA

)

(1)

(2)

(3)

(4)

(5)

Exc

ludi

ngbl

ue-c

olla

r

Pen

sion

s(d

)7.

44**

(2.7

3)8.

93**

(2.8

3)7.

40**

(3.1

8)6.

23**

(3.2

0)6.

46**

(3.2

0)7.

22*

(3.7

2)O

neim

med

iate

supe

rvis

or(m

)4.

08(3

.83)

2.95

(3.8

9)3.

80(3

.94)

4.20

(3.9

5)4.

48(3

.95)

5.51

(4.7

0)N

otst

rong

lylo

yalt

oem

ploy

er(b

)-5

.76*

*(2

.57)

-5.6

6**

(2.6

1)-5

.87*

*(2

.68)

-6.3

1**

(2.6

8)-5

.29*

(2.7

2)-6

.85*

*(3

.20)

Fre

quen

tla

yoff

sat

curr

ent

empl

oyer

(b)

4.34

(3.5

9)3.

98(3

.69)

3.96

(3.7

9)4.

83(3

.79)

5.25

(3.7

8)1.

46(4

.76)

Eas

yto

find

new

job

(f)

2.94

(2.3

4)0.

78(2

.41)

-0.0

2(2

.51)

-0.0

4(2

.52)

-0.5

2(2

.53)

-1.8

3(2

.90)

Log

ofot

her

inco

me

(h)

0.31

(0.2

2)-0

.01

(0.3

2)-0

.05

(0.3

3)-0

.04

(0.3

3)-0

.01

(0.3

3)0.

07(0

.38)

Dem

ogra

phic

cont

rols

Age

...

-0.5

9**

(0.1

1)-0

.62*

*(0

.12)

-0.6

5**

(0.1

2)-0

.64*

*(0

.12)

-0.6

6**

(0.1

4)F

emal

e..

.10

.47*

*(2

.38)

12.1

7**

(2.6

1)13

.62*

*(2

.67)

13.7

7**

(2.6

6)15

.49*

*(2

.94)

Mar

ried

...

3.03

(3.5

8)2.

52(3

.60)

2.27

(3.6

1)1.

97(3

.62)

2.32

(4.2

6)C

hild

ren

inho

useh

old

...

1.94

(2.4

7)1.

09(2

.50)

0.90

(2.5

0)0.

89(2

.50)

0.27

(2.8

2)N

on-w

hite

...

-4.9

3*(3

.00)

-5.1

9*(3

.05)

-4.9

4(3

.05)

-4.8

5(3

.06)

-6.2

1*(3

.49)

Hig

hsc

hool

grad

uate

...

-5.3

4(5

.18)

-5.1

5(5

.36)

-5.8

2(5

.36)

-5.4

3(5

.39)

-4.5

1(8

.09)

Col

lege

grad

uate

...

3.04

(5.4

4)1.

89(5

.97)

-0.6

5(6

.08)

-0.1

6(6

.10)

1.35

(8.5

5)Jo

bco

ntro

ls3–

5ye

ars

ofte

nure

...

...

9.44

**(3

.16)

9.38

**(3

.17)

9.70

**(3

.16)

10.1

7**

(3.5

4)6–

10ye

ars

ofte

nure

...

...

10.4

9**

(3.2

9)9.

82**

(3.3

2)10

.25*

*(3

.31)

10.3

8**

(3.7

0)11

–20

year

sof

tenu

re..

...

.1.

74(3

.74)

0.46

(3.7

9)0.

83(3

.79)

4.01

(4.3

0)21

year

sor

mor

eof

tenu

re..

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43(4

.82)

0.81

(4.9

2)1.

30(4

.92)

1.90

(5.7

0)F

ull-

tim

e..

...

.5.

79(3

.75)

5.21

(3.7

5)5.

48(3

.76)

6.77

*(4

.03)

Uni

on..

...

.1.

43(3

.47)

1.08

(3.4

9)1.

00(3

.49)

4.88

(4.3

8)T

rain

ing

...

...

-0.5

6(2

.58)

-0.8

6(2

.58)

-1.5

2(2

.58)

-1.8

0(2

.95)

Lar

gees

tabl

ishm

ent

...

...

1.50

(3.0

7)0.

80(3

.09)

0.91

(3.0

9)4.

43(3

.48)

Pub

licse

ctor

esta

blis

hmen

t..

...

.-6

.02*

(3.4

1)-5

.31

(3.4

2)-5

.22

(3.4

1)-6

.10

(3.7

6)L

ogof

hour

lyea

rnin

gs..

...

...

.5.

40**

(1.8

6)5.

16**

(1.8

6)4.

37**

(2.1

4)C

hanc

efo

rad

vanc

emen

thi

gh..

...

...

...

.5.

99**

(2.7

3)5.

90*

(3.0

5)In

clud

esoc

cupa

tion

dum

mie

sY

esY

esY

esY

esP

seud

o-R

20.

010.

030.

040.

050.

050.

05N

2057

1584

Not

es:

Est

imat

edm

argi

nale

ffec

tsar

em

ulti

plie

dby

100.

Stan

dard

erro

rsar

ere

port

edin

the

pare

nthe

ses.

All

esti

mat

esar

ew

eigh

ted

usin

gN

SCW

sam

plin

gw

eigh

ts.B

lue-

colla

rw

orke

rsar

epr

oduc

tion

,ope

rato

rsan

dre

pair

wor

kers

inth

eN

SCW

.An

ellip

sis

indi

cate

sa

vari

able

excl

uded

from

the

spec

ifica

tion

.*

deno

tes

sign

ifica

nce

atth

e0.

10le

vel,

and

**de

note

ssi

gnifi

canc

eat

the

0.05

leve

l.



of the tenure coefficients suggests that those in the middle of their tenure arethe most likely to report high effort consistent with a Lazear type model ofdeferred compensation in which quasi-rents are built up early in tenure andthen paid back later in tenure. Full-time workers are more likely to reporthigh effort, corresponding to the fact that they have more at risk from losingtheir job. Training does not appear to affect effort. Those in the public sectorare less likely to report high effort. Again, despite several new significantcoefficients and the improved explanatory power of the estimation (as mea-sured by the pseudo-R2), the pattern of results from the model remains largelyunchanged.

In the next column, we add the current earnings variable. The log ofhourly earnings is a positive and statistically significant determinant ofreporting high effort. As the influence of the wage is independent of thatof the wage determinants already included, it might well be considered theinfluence of an efficiency wage. Yet, the presence of the wage does little toreduce the role of pensions. Interestingly, as the coefficients on the wage andon the pension variable are roughly the same order of magnitude and as thewage is measured in logs, the wage would have to more than double (increaseby one log wage) to equal the influence of the deferred compensation proxy.Thus, as important as efficiency wages may be in some circumstances (seeAnsar et al., 1997), they seemingly leave room for the strategic use of deferredcompensation.

In Column (5), we add the proxy for the presence of tournament payschemes. As expected, workers anticipating consideration for promotion(advancement) in their workplace are more likely to exert effort. Nonetheless,the effect of pensions remains strong and significant. After our best attemptsto control for other determinants of effort, pensions continue to play a roleconsistent with their strategic use as deferred compensation.

In Table 4, we repeat the analysis using the AWIRS.7 Despite the factthat we now measure deferred compensation with a proxy for the slope of thetenure–earnings profile, results are similar to those of Table 3. Deferredcompensation increases effort, and the coefficient estimates are significant atthe 0.10 level even as the full set of controls are added. Note that the positivecoefficient estimates on the log of weekly earnings and the chance of a moresenior position in Column (5) indicate that the proxies for efficiency wagesand tournament schemes, respectively, are behaving as anticipated by theoryand as they did in the NSCW data.

The estimates indicate that a one-unit increase in the tenure–wage ratioincreases the probability of high effort by 10–12 percentage points yet such an

7For these individual-level estimations some variables are measured at the workplace level.Moulton (1990) shows in the linear case that the standard errors of the coefficient estimatescan be understated. In both the linear case and nonlinear probit case, a solution is toestimate models allowing for arbitrary correlation within groups through clustering(Wooldridge, 2006). We follow this solution in all estimates using the AWIRS.



Ta

ble

4D

eter

min

an

ts

of

Wo

rk

erE

ffo

rt

,Ma

rg

ina

lE

ffec

ts

fro

ma

Pr

ob

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od

el,A

WIR

S(A

ust

ra

lia

)

(1)

(2)

(3)

(4)

(5)

Exc

ludi

ngbl

ue-c

olla

rw

orke

rs

Ten

ure–

wag

era

tio

(d)

11.5

3*(6

.10)

11.9

2**

(5.9

4)9.

74(6

.04)

10.5

5*(5

.90)

10.6

9*(5

.86)

15.2

8**

(5.8

1)F

orm

alev

alua

tion

s(m

)1.

72**

(0.8

9)1.

30(0

.85)

0.82

(0.7

9)0.

78(0

.79)

0.79

(0.7

8)-0

.21

(0.8

4)In

secu

reab

out

futu

reat

job

(b)

-4.1

5**

(0.7

8)-4

.13*

*(0

.76)

-3.5

1**

(0.7

4)-3

.51*

*(0

.75)

-3.0

0**

(0.7

3)-3

.23*

*(0

.81)

Rec

ent

dism

issa

lsat

wor

kpla

ce(b

)-1

.22*

(0.7

2)-1

.16*

(0.6

9)-0

.77

(0.7

0)-0

.80

(0.7

1)-0

.73

(0.7

0)-0

.24

(0.7

2)D

emog

raph

icco

ntro

lsA

ge..

.0.

26**

(0.0

3)0.

26(0

.04)

0.26

**(0

.04)

0.26

**(0

.04)

0.23

**(0

.04)

Fem

ale

...

5.33

**(0

.66)

5.21

**(0

.76)

5.40

**(0

.77)

5.40

**(0

.77)

5.56

**(0

.78)

Chi

ldre

nin

hous

ehol

d..

.-2

.33*

*(0

.65)

-2.0

8**

(0.6

4)-2

.00*

*(0

.64)

-2.0

3**

(0.6

4)-1

.86*

*(0

.70)

Non

-Aus

tral

ian

...

-1.3

6*(0

.77)

-0.8

6(0

.73)

-0.8

5(0

.73)

-0.7

9(0

.72)

-0.7

8(0

.82)

Hig

hsc

hool

grad

uate

...

-1.1

0(0

.77)

-2.3

0**

(0.9

1)-2

.42*

*(0

.91)

-2.3

0**

(0.9

2)-0

.95

(0.9

5)C

olle

gegr

adua

te..

.1.

85**

(0.8

2)-1

.75

(1.3

0)-1

.89

(1.3

0)-1

.69

(1.3

2)-0

.48

(1.2

4)Jo

bco

ntro

ls3–

5ye

ars

ofte

nure

...

...

-0.0

2(0

.81)

-0.1

8(0

.81)

-0.0

4(0

.80)

-0.4

8(0

.87)

6–10

year

sof

tenu

re..

...

.-0

.24

(0.9

0)-0

.41

(0.9

1)-0

.28

(0.8

9)0.

28(0

.98)

11–2

0ye

ars

ofte

nure

...

...

-0.7

4(1

.22)

-1.0

0(1

.23)

-0.8

2(1

.21)

0.14

(1.2

5)21

year

sor

mor

eof

tenu

re..

...

.-0

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(1.8

3)-0

.64

(1.8

5)-0

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(1.8

1)0.

38(2

.03)

Ful

l-ti

me

...

...

-0.9

0(0

.90)

-1.9

3*(0

.98)

-1.7

8*(0

.99)

-0.8

4(1

.10)

Uni

on..

...

.-1

.26*

(0.7

2)-1

.35*

(0.7

2)-1

.15

(0.7

2)-1

.17

(0.7

4)T

rain

ing

...

...

1.57

**(0

.72)

1.47

**(0

.73)

0.96

(0.7

4)0.

83(0

.75)

Lar

gees

tabl

ishm

ent

...

...

-1.0

7(0

.91)

-1.1

3(0

.92)

-1.1

3(0

.91)

-1.3

2(0

.98)

Pub

licse

ctor

...

...

-1.6

5(1

.29)

-1.5

9(1

.31)

-1.7

5(1

.32)

...

Log

ofw

eekl

yea

rnin

gs..

...

...

.1.

75*

(0.9

3)1.

41(0

.93)

0.92

(0.9

9)C

hanc

esof

gett

ing

am

ore

seni

orjo

b..

...

...

...

.4.

70**

(0.8

3)4.

16**

(0.7

0)In

clud

esoc

cupa

tion

dum

mie

sY

esY

esY

esY

esP

seud

o-R

20.

010.

030.

040.

050.

050.

05N

11,9

6184

51

Not

es:

Est

imat

edm

argi

nale

ffec

tsar

em

ulti

plie

dby

100.

Stan

dard

erro

rsar

ere

port

edin

the

pare

nthe

ses

and

are

corr

ecte

dto

allo

wfo

rno

nind

epen

denc

eof

obse

rvat

ions

wit

hin

aw

orkp

lace

.All

esti

mat

esar

ew

eigh

ted

usin

gA

WIR

Ssa

mpl

ing

wei

ghts

.Blu

e-co

llar

wor

kers

are

labo

urer

s,pl

antw

orke

rsan

dtr

ades

pers

ons

inth

eA

WIR

S.A

nel

lipsi

sin

dica

tes

ava

riab

leex

clud

edfr

omth

esp

ecifi

cati

on.

*de

note

ssi

gnifi

canc

eat

the

0.10

leve

l,an

d**

deno

tes

sign

ifica

nce

atth

e0.

05le

vel.



increase in the tenure–wage ratio is highly unlikely. It would be more rea-sonable to consider a two standard deviation increase from the mean ratio of1.0227. This increase of 0.11 is associated with an increased probability ofreporting high effort of 1.18 percentage points given the Column (5) estimate(1.18 = 0.11 ¥ 10.69). Alternatively, given that only 11 per cent of the samplereports low effort, the estimate suggests that about 11 per cent of those loweffort workers would increase their effort level.

The remaining estimates in Table 4 confirm the model’s other first-orderpredictions. Formal evaluation increases effort but the effect is not signifi-cant. Insecurity and recent dismissals, proxies for the probability of exog-enous job separation, both reduce effort as expected. This differs from theNSCW results. The indicator of insecurity about one’s future has a consis-tently significant negative effect on effort and will be our preferred measure ofseparation probability (along with lack of loyalty in the NSCW) in oursubsequent analyses.

The basic demographic and job control variables have effects on effortthat are similar to those from the NSCW in Table 3 with a few exceptions.The age variable has a different effect on effort in the two countries. Childrenin the household reduce worker effort in Australia but had no effect in theUSA. Union workers put forth less effort in Australia, but not in the USA.We emphasize, however, that even in the specifications including the full setof controls, deferred compensation increases effort in both the USA andAustralia.

5.2 Caveats and Additional Estimation

In this section we discuss the limitations of our basic results and providerobustness checks. We recognize that a basic concern with our approach isthat unmeasured factors may simultaneously cause both deferred compensa-tion and higher effort biasing our estimates. As an illustration, to the extentthat our measures of occupation and training do not capture on-the-job skillacquisition, such skills may both cause the appearance of deferred com-pensation and be associated with greater effort requirements. Instrumentalvariable approaches might help control for such unmeasured factors byidentifying exogenous variation in deferred compensation. We searched forinstruments in both data sets and tested models using these instruments. Ourattempts yielded results in both the USA and the Australian data that werebroadly supportive of the model prediction regarding deferred compensation,but we are highly skeptical of the only instruments our limited data allowed.In the US data, we used the availability of employer-provided insurance in anestimation of a bivariate probit allowing both effort and pensions to beendogenous. Health insurance provision and pension provision are highlycorrelated, and the model yielded results that showed a large and positivepension–effort relationship. The new estimates were perhaps too large to be



sensible, however, and the large standard errors raised doubts as to thesuitability of the instrument. It may be the case that health insurance itself isa form of deferred compensation given that it becomes more valuable withage. Thus, it may not be an exogenous source of variation. A similar problemarose with the Australian data.8 In sum, these efforts raised no doubts as toour underlying findings but were not measured precisely enough to provecausation.

An additional concern is that the effort requirement question in the USdata may not capture discretionary effort responses and may simply measurethe difficulty of the job. In turn this difficulty may be greater in jobs that areeasily monitored and in which effort is determined by machine speed or anassembly line. If unmeasured but correlated factors cause these jobs to havedeferred compensation, our estimates are spurious. Although we cannot rulethis out, we did limit our sample to eliminate all manual (blue-collar) jobs.These are most likely to have effort easily monitored and set by machines.These results are shown in the final columns of both Tables 3 and 4. Theycontinue to show a role for deferred compensation. The marginal effects are,if anything, larger but the standard error is also larger in the case of US data.

We also tested an alternative to the basic effort measure using a supple-mentary variable in the US data asking if workers put forth effort that ‘isbeyond what is required’. If workers indicate both that their jobs require higheffort (the measure used throughout the paper) and that they work harderthan required, we identify their effort as high. Although the effects usingthis dependent variable emerge as slightly weaker, deferred compensationremains a positive determinant of effort (statistically significant at the 0.10level) and the second-order effects continue to be statistically significant andbroadly supportive of the model. This more refined effort measure suggests adurable tendency in the data and hints that the subjective measures maycapture something beyond immutable job requirements.

More generally, we recognize that many of the variables we use remainself-reported and might be endogenous. The measures of effort and loyaltymay be largely driven by outside factors and, even if not, the direction ofcausation may be unclear. We must remain content in such cases with durablecorrelations that are supportive of the theoretical model and not claim tohave proven the implied causation.

In testing the durability of correlations, we note that the proxy of thetenure–wage profile computed from the AWIRS compares workers withmore and less than five years of tenure. This is approximately the mean acrossestablishments but we reestimated the results in Table 4 using various alter-native cut points, including three, four, six, seven and eight years of tenure.

8The instruments for age–earnings profiles we used (the percentage of workers on fixed contracts,the percentage of permanent workers and the percentage of casual workers) are likely notexogenous sources of variation.



The results were all broadly similar and, indeed, some cutoffs were moresupportive of the role of deferred compensation than those we present.

5.3 Second-order Effects

Our model yielded a second-order prediction that the influence of deferredcompensation on effort wanes as the exogenous probability of job separationgrows. To test this prediction we use our preferred measures from the NSCWand AWIRS of the likelihood of job separation. Tables 3 and 4 confirmempirically the first-order negative effect of lack of loyalty and insecurityabout one’s future at the job on effort. To test the second-order prediction,we divide our samples and reestimate the full specifications from Column (5)of Tables 3 and 4.

The estimates from the NSCW in the top panel of Table 5 show thatpensions are associated with a significantly increased probability of effortamong loyal employees. Loyal employees with a pension are nearly 10 per-centage points more likely to report high effort. Pensions play no role oneffort among workers not reporting employer loyalty. The resulting coeffi-cient is negative and smaller than its standard error. These results fit themodel’s prediction. This difference in pension effects between those with high

Table 5Determinants of Effort, Results by Separation Likelihood

Not loyal toemployer (NSCW)

or job notsecure (AWIRS)

Loyal toemployer (NSCW)

or job secure(AWIRS)

p value of atest of the equalityof the coefficients

NSCW (USA)Pensions (d) -4.02 (6.26) 9.63** (3.74) 0.06One immediatesupervisor (m)

9.02 (7.52) 3.88 (4.66) 0.57

Frequent layoffs at currentemployer (b)

9.54 (6.54) 3.09 (4.69) 0.44

Easy to find new job ( f) -1.77 (5.02) -0.30 (2.96) 0.80Log of other income (h) 0.36 (0.64) -0.10 (0.39) 0.54Pseudo-R2 0.07 0.05Sample 562 1495

AWIRS (Australia)Tenure–wage ratio (d) 2.01 (11.91) 12.47** (6.27) 0.29Formal evaluations (m) 1.61 (1.55) 0.47 (0.82) 0.60Recent dismissals atworkplace (b)

-1.04 (1.37) -0.66 (0.78) 0.94

Pseudo-R2 0.04 0.06Sample 3536 8425

Notes: All controls are included. See Column (5) of Tables 3 and 4 for the complete list. Marginal effects froma probit model multiplied by 100 are reported with standard errors in parentheses. All estimates are weightedusing NSCW or AWIRS sampling weights. The AWIRS standard errors are corrected to allow for noninde-pendence of observations within a workplace.** denotes significance at the 0.05 level.



loyalty and those with low loyalty is statistically significant at the 0.10 level (pvalue = 0.06) as calculated in a fully stacked specification that uses the entiresample and interacts each variable.9

In the bottom panel, we repeat the analysis using the AWIRS. A steepertenure–wage profile has a significant positive effect on effort among thosesecure. The effect among workers not secure is small and insignificant.Although the difference in the coefficient estimates is not significant due tothe imprecision of the coefficient estimates, the finding of an effect only in thesubsample of secure workers remains consistent with our model’s prediction.The results from both countries suggest that deferring compensation has littleor no influence on worker effort when the likelihood of exogenous job loss ishigh.10

We also conduct a further test related to the ‘terminal period’ issueisolated when presenting the model. Once retained, workers will be repaidtheir deferred compensation and it stops motivating effort. While we do notknow when such a guarantee happens for any particular worker, we antici-pate it is highly correlated with years of tenure. Senior workers are fullyvested in their pensions and have less to fear from job loss. The likelihood ofpartnership or tenure increases with seniority. More generally, seniority maybe officially associated with an increasing job right (especially in a unionizedsetting) or a sufficient number of years of positive appraisals may create a defacto job right. In all of these cases, more senior workers should be less wellmotivated by what otherwise looks like deferred compensation. What isgenuinely at risk for junior workers if they shirk is more nearly guaranteedfor senior workers.

To test for terminal period effects, we split the samples by worker tenureundertaking separate estimations for those above or below the mean levels oftenure. We continue to use the most complete specification from Column (5)of Tables 3 and 4. The new results in Table 6 continue to reinforce the basicmodel predictions but show some additional variations. Most importantly,only those with low levels of tenure remain more likely to report high effortwhen compensation is deferred. In the NSCW, junior workers are more than9 percentage points more likely to report high effort when they receive apension. The analogous coefficient for senior workers is negative with a verylarge standard error. The difference in the coefficients is statistically signifi-cant at the 0.10 level. Although the difference is not significant in the AWIRSdata, the steep tenure–wage profile clearly motivates only the junior workers.

9Marginal effects from the stacked estimate are not markedly different from those separatelyestimated.

10We note that the theoretical model predicts other second-order effects on effort but, as thefirst-order effects of monitoring and spousal income were not significant, we do not con-centrate on them. We note that consistent with those second-order predictions pensionshave a larger effect on effort in the face of a positive monitoring in the US data but that thisis not replicated in the Australian data.



Thus, just as the terminal period effect predicts, those more likely to beguaranteed repayment of their deferred compensation are less likely to bemotivated to put forth effort.

One other interesting result emerges from Table 6. Senior workersappear more likely to respond with greater effort when monitoring is presentthan do junior workers, although the difference falls just short of significancein both the US and Australian data. Such a result might well follow ifdeferred compensation no longer motivates senior workers. The alternativeof direct monitoring is required to induce effort.11

6 Conclusions

This paper attempts to fill a gap in the testing of the model of strategicdeferred compensation. We present an illustrative model of such a strategy

11It might be thought that performance-related pay would reduce the impact of deferred com-pensation as it is a substitute. Our only measure of performance-related pay exists in theAustralian data and is unfortunately measured at the establishment-wide level. While itsinteraction with deferred compensation generated the anticipated negative coefficient, itwas insignificant. Moreover, the coefficient on performance pay itself also emerged asnegative, the opposite of what would be predicted. The quality of the variable makes ussuspicious of its reliability and of drawing firm conclusions regarding the role of perfor-mance pay.

Table 6Determinants of Effort, Results by Seniority Level

Junior Senior

p value of atest of the equalityof the coefficients

NSCW (USA)Pensions (d) 9.62** (3.56) -4.80 (7.64) 0.09One immediate supervisor (m) 0.16 (4.97) 13.08* (6.75) 0.13Not strongly loyal to employer (b) -6.95** (3.39) -2.38 (4.58) 0.42Frequent layoffs at currentemployer (b)

6.11 (4.40) 2.68 (7.52) 0.69

Easy to find new job ( f) -3.13 (3.27) 3.79 (4.06) 0.19Log of other income (h) -0.17 (0.44) -0.01 (0.51) 0.81Pseudo-R2 0.05 0.08Sample 1,310 747

AWIRS (Australia)Tenure–wage ratio (d) 12.50 (7.97) 1.75 (8.60) 0.42Formal evaluations (m) 0.06 (1.06) 1.61 (1.02) 0.24Insecure about future at job (b) -3.77** (0.98) -1.95* (1.12) 0.36Recent dismissals at workplace (b) -0.63 (0.93) -1.02 (1.00) 0.71Pseudo-R2 0.05 0.07Sample 7017 4944

Notes: All controls are included. See Column (5) of Tables 3 and 4 for the complete list. Marginal effects froma probit model multiplied by 100 are reported with standard errors in parentheses. All estimates are weightedusing NSCW or AWIRS sampling weights. Junior workers are those with less than the mean years of tenurein the respective data sets (see Tables 1 and 2). The AWIRS standard errors are corrected to allow fornonindependence of observations within a workplace.* denotes significance at the 0.10 level, and ** denotes significance at the 0.05 level.



emphasizing that deferred compensation increases the cost of job loss and soincreases the effort of workers. Our testing methodology has been to useself-reported measures of effort to examine if the variation across workers inthose measures corresponds to that predicted by our illustrative model. Onbalance, the evidence from our samples of US and of Australian workerssupports the importance of deferred compensation. The effect of deferredcompensation remains even after we control for individual and job charac-teristics, as well as individual wage levels and self-assessed chances of pro-motion. We view the latter control as a proxy for the presence of tournamentpay schemes. We view the former as a control for the presence of efficiencywage schemes (after also controlling for standard wage determinants). Themajor weakness of our statistical examination of deferred compensationremains the possibility of omitted variable bias that may drive both ourcritical independent and dependent variables.

The remaining predictions of the model met with mixed success. Ourproxy for monitoring routinely took a positive coefficient but was typicallyinsignificant. The use of layoffs was a less consistent determinant with thesign showing variation. Lack of loyalty to an employer in the NSCW andfeelings of insecurity about one’s future at his job in the AWIRS wereassociated with significantly reduced worker effort, especially among juniorworkers. We suggest that lack of loyalty and insecurity reflects, in part, ahigher chance of separation among workers in our respective samples. Wefound little support for reemployment probabilities influencing effort inthe NSCW. We also found no support for the role of spouse’s income butspeculate this may be due to the strength of assortative matching in themarriage market. Some of the strongest results emerged in testing the second-order prediction that deferred compensation’s effect on effort shrinks withthe probability of exogenous separation. When we divided our sample byself-assessed loyalty and security about one’s future at the job, the positiveeffect of deferred compensation on effort was only present in the samples ofworkers with low likelihoods of job separation. Moreover, the effects ofdeferred compensation on effort were much stronger for workers with lowtenure than workers with high tenure. These results are consistent with thepredictions of our model and the strategic deferral of compensation.

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