Have Lazear-Style Implicit Contracts Disappeared? 

David J. Balan 

Bureau of Economics 

Federal Trade Commission 

January 2003

PRELIMINARY DRAFT: DO NOT CITE OR QUOTE 

I am grateful for helpful comments from Ishan Ajwad, Lee Alston, John Deke, George Deltas, Kevin 

Hallock, Wally Hendricks, Phee Kahl, David Kaplan, Daiji Kawaguchi, Robert Moore, David Neumark, 

Anne Polivka, David Reiffen, and Jay Stewart on issues related to this paper. The views expressed in this 

paper are those of the author and do not necessarily represent the views of the Federal Trade Commission 

or of any individual Commissioner.
1

Have Lazear-Style Implicit Contracts Disappeared? 

Abstract 

A well-known theory (Lazear, 1979) argues that wage patterns in which younger workers are 

underpaid relative to marginal revenue product and older workers are overpaid relative to marginal 

revenue product can be understood as an implicit contract designed to combat principal-agent problems in 

environments where worker monitoring is costly. In this paper I argue that a number of recent 

developments (most notably the legal ban on mandatory retirement) have caused the formation of these 

implicit contracts between firms and young workers to decline (or cease). I derive testable implications 

of this hypothesis and test it using a Panel Study of Income Dynamics sample of prime-age, full-time, 

private sector, non-union male workers who are not self-employed. This is the group that, according to 

Lazear’s theory, is most likely to be party to these implicit contracts. The results are consistent with the 

hypothesis. In order to explore the question of whether the results from the main sample have some cause 

other than the hypothesized one, I perform the following exercise: I identify other groups in the data (both 

sub-groups of the main (male) sample and a separate female sample); determine what the hypothesis 

predicts for those groups; and then perform tests similar to those performed on the main sample to see 

how well the hypothesis holds up. The results from the male sub-groups do not support the hypothesis, 

but do not strongly refute it. This lack of support may indicate that the hypothesis is false, but it also may 

be due to the crudeness of the method for assigning workers to the different sub-groups. The results from 

the female sample support the hypothesis, but the strength of this support depends on the regression 

specification used.

David J. Balan 

Bureau of Economics 

Federal Trade Commission 

600 Pennsylvania Avenue 

Washington, DC 20580 

(202) 326-3214 (office) 

dbalan@ftc.gov 
2

I. Introduction:

In a well-known article, Lazear (1979) develops a model in which certain types of workers 

enter into implicit contracts with their employers (hereafter termed “Lazear-Style Implicit 

Contracts” or LSICs). Under the terms of these LSICs workers are underpaid early in their 

careers (relative to their marginal revenue product) and overpaid late in their careers. The theory 

purports to explain the existence of mandatory retirement and the fact that wages often increase 

more quickly than does productivity.1

 Lazear’s theory has been tested empirically (Lazear, 

1979; Lazear & Moore, 1984; Goldin, 1986; Hutchens, 1987; Hellerstein, Neumark, & Troske, 

1999; Lazear, 2000), and has amassed a substantial degree of support.2

 Most of these tests, 

however, use older data. Lazear used data from 1969-71, Lazear & Moore from 1978, Goldin 

from 1890-1940, and Hutchens from 1971. 

The purpose of this paper is to develop and test (using more recent data) the following 

hypothesis: 

 

Hypothesis: The formation of LSICs between firms and new entrants into the labor market has 

declined (or ceased).

In his original (1979) paper, Lazear argued that a mandatory retirement provision was 

necessary for the formation of LSICs. If this were true, then the amendments to the Age 

Discrimination in Employment Act (ADEA) of 1978 and 1986 (which made almost all forms of 

mandatory retirement illegal) should have been sufficient to bring about the hypothesized result. 

 

1

 For a summary of the theory and its implications, see Lazear (2000). 2

 Hellerstein, Neumark, & Troske (1999) find that wages generally track productivity, which is inconsistent with the 

existence of LSICs. But their data come only from the manufacturing sector, which may be less likely to employ 

LSICs. 
3

But later theoretical work by Lazear (1983) showed that a properly designed defined-benefit 

pension plan can serve as a substitute for a mandatory retirement provision in an LSIC. It is 

therefore possible for LSICs to persist despite the ban on mandatory retirement. But the massive 

shift in recent years away from defined-benefit plans in favor of defined-contribution plans 

(which cannot support LSICs) seems to suggest that this has not happened. 

Why have defined-benefit plans not been employed to “rescue” LSICs? First, such a rescue is 

costly. A variety of factors influence the optimal structure of a defined-benefit pension plan. 

Modifying the (formerly optimal) plan in order to make it support LSICs must entail some costs. 

Second, it may be that the benefits of LSICs are not as great as they once were. 

One problem with LSICs is that they tend to inhibit efficient separations. A worker who is 

party to an LSIC may have an incentive to remain with his3

 current job instead of moving to a 

job in which he is more productive because he is expecting a future overpayment from his 

current employer. Similarly, a firm may retain a worker (particularly an older worker) that it 

wishes to dismiss for fear of acquiring a reputation for reneging.4

 Since LSICs tend to limit 

efficient separations, anything that makes efficient separations more likely (i.e., anything that 

makes workers or firms more likely to prefer to separate), reduces the benefits of LSICs and so 

makes them less likely to be formed. A worker who expects that he will want to leave his job, or 

will be compelled to leave, is less likely to be willing to enter into an LSIC. Efficient separations 

are more likely when workers find moving less distasteful, because alternative job offers are 

more likely to be perceived as superior. They are also more likely when firms have access to 

 

3

 Lazear contracts are typically associated with males. The more complicated labor supply behavior of females tends 

to work against the formation of long-term implicit contracts. 

4

 Since older workers are overpaid relative to their marginal revenue product, firms have an incentive to renege on 

the LSIC by firing them or withholding the promised overpayment. Reputation effects are typically assumed to 

limit such reneging. 
4

technologies and/or organizational structures that rely less heavily on firm-specific human 

capital. 

LSICs contribute to positive measured returns to firm tenure (i.e., they contribute to the 

positive effect of an increase in firm tenure on wages). An implication of the Hypothesis, 

therefore, is that firm tenure should decline. A further implication is that this decline should be 

greater for less experienced workers. To see this, consider two dates: one shortly before the 

hypothesized effect began and one shortly after. The incidence of LSICs among experienced 

workers will be the same on these two dates, while the incidence of LSICs for new entrants to 

the labor force will be smaller at the later date. This means that, all else equal, the returns to firm 

tenure among older workers should be the same on the two dates, whereas returns to firm tenure 

among younger workers should be lower in the later period. 

This empirical implication can be taken to the data: the main test of the Hypothesis consists of 

using a 1981-1992 sample from the Panel Study of Income Dynamics (PSID) to estimate a wage 

equation for the main sample (prime-age, full-time, private sector, non-union male workers who 

are not self-employed). This is the group that, according to Lazear’s theory, is most likely to be 

party to LSICs. In order to explore the question of whether the main results have some cause 

other than the hypothesized one, I perform the following exercise: I identify other groups in the 

data (both sub-groups of the full (male) sample and a separate female sample); determine what 

the Hypothesis predicts for those groups; and then perform empirical tests similar to those 

performed on the main sample to see how well the Hypothesis holds up. 

The remainder of the paper is organized as follows: Section II provides a background to 

Lazear’s theory; Section III develops the Hypothesis that the formation of LSICs with new 

entrants to the labor force has declined or ceased; Section IV develops testable empirical 
5

implications of the Hypothesis; Section V describes the data and provides summary statistics; 

Section VI describes the estimation procedure and presents the main empirical results; Section 

VII discusses alternative possible explanations for the main empirical results and presents 

additional results; and Section VIII concludes. 

II. Theoretical Background:

There is evidence that tenure-earnings profiles have steeper slopes than tenure-productivity 

profiles. (Medoff & Abraham 1980, 1981; Kotlikoff & Gokhale, 1992). This suggests that 

something other than human capital accumulation may be driving observed tenure-earnings 

profiles.5

 Lazear (1979) develops a theory in which tenure-earnings profiles6 that are steeper 

than tenure-productivity profiles are a property of implicit contracts that exist between workers 

and firms. 

These implicit contracts exist to address the following effort-elicitation problem:7

 suppose 

that the efficient labor contract (i.e., the contract that maximizes total social surplus) would 

stipulate high employee effort and high pay. Ideally, workers and firms could write a contract 

with these features. The problem, however, is that some types of workers are difficult to monitor 

so it is difficult to detect shirking by these workers. There is no full-employment equilibrium in 

which such workers are paid a wage equal to what their spot marginal revenue product (MRP) 

would be if they exerted high effort. If this were the prevailing wage rate, all workers would 

 

5

 Other possible explanations for this phenomenon have been suggested. Social norms of fairness may dictate that 

workers should “move up the ladder” as they acquire greater tenure. Alternatively, workers may desire rising 

consumption over their lifetimes but be unable to discipline themselves to save (Neumark, 1995). 

6

 Discussions of Lazear’s theory have alternately referred to age-earnings profiles, experience-earnings profiles, and 

tenure-earnings profiles. In the context of the theory, these are all equivalent because workers are assumed to 

remain with the same firm for their entire careers. But the focus is rightly on firm tenure because LSICs involve the 

relationship between the worker and the specific firm. 7

 Actually, this mechanism can be employed to combat any kind of principal-agent problem where the agent’s 

actions are not fully observable to the principal. 
6

shirk since they would know that if they were caught and fired, they could find another job at the 

same wage. In light of this fact, firms would be unwilling to offer that wage. If workers are 

unable to commit to high effort, then both workers and firms will be forced into inefficient 

contracts which are characterized either by worker shirking (and a correspondingly low wage) or 

by a high level of costly monitoring. 

 Lazear argues that LSICs arose to combat this problem. Workers are paid below their MRP 

in the early years of their careers. The difference between pay and MRP in the early years of a 

career is held by the firm as a “bond” and is repaid in the form of compensation above MRP in 

the later years if the worker is still employed by the firm. If the worker shirks and is fired, he 

forfeits the bond.8

 

Using LSICs to induce employee effort can be superior to exclusive reliance on employee 

monitoring. Firms can maintain a given level of deterrent by making the tenure-earnings profile 

steeper (which makes the performance “bond” larger) and making an appropriate reduction in the 

probability of catching shirkers (i.e., by monitoring less). 

There is a limit to the extent that LSICs can be used as a substitute for monitoring because 

steeper tenure-earnings profiles make it more difficult for firms to credibly commit to honoring 

their commitments to older workers. Furthermore, in order to attract workers, firms using LSICs 

will have to offer higher expected pay as compensation for the risk that the firm will renege.9

 

For this reason, (feasible) LSICs are not always superior to costly monitoring. Since LSICs and 

monitoring are substitutes, the theory predicts that LSICs will be more attractive (and more 

common) in occupations where employee effort is more difficult to monitor. 

 

8

 Workers have a greater incentive to cheat (or shirk) if the bond is small, and firms have a greater incentive to 

renege (by withholding the promised overpayment) if it is large. In equilibrium, there is still some cheating and 

some reneging. 
7

Lazear’s theory has been tested by a number of researchers (Lazear, 1979; Lazear & Moore, 

1984; Goldin, 1986; Hutchens, 1987; Hellerstein, Neumark, & Troske, 1999; Lazear, 2000). The 

evidence, while mixed, provides a measure of empirical support for the theory. 

III. The Decline or Demise of LSICs:

The purpose of this section is to present arguments in favor of the proposition that the costs 

associated with LSICs have increased and the benefits have decreased. This proposition, if 

correct, leads directly to the Hypothesis of the paper: that the formation of LSICs between firms 

and young workers has declined or ceased. 

A. Increasing Costs of LSICs 

In his original 1979 paper, Lazear argues that mandatory retirement provisions are necessary 

in order to sustain LSICs, and can be understood to exist for this purpose. Workers and firms 

bargain ex-ante to an efficient LSIC, in which the separation between the worker and the firm 

takes place at the efficient date (when the worker’s MRP drops below the value of time spent in 

retirement). But the LSIC must make retirement mandatory at the efficient date. Otherwise the 

worker will choose not to retire at the efficient date as his pay at that date would exceed his 

MRP, and therefore would exceed the value of time spent in retirement. If the worker did not 

retire, then the size of the repayment (in the form of overpayment when old) would exceed the 

amount of the bond (in the form of underpayment when young). This would make firms 

unwilling to enter into LSICs. In other words, mandatory retirement is a device that allows the 

 9

 Reneging can be exogenous (the firm might go out of business), and it can also be endogenous (some firms receive 

a particularly high benefit from reneging). 
8

worker to credibly commit not to engage in behavior that, rationally anticipated by the firm, 

would make it unwilling to enter into the (mutually beneficial) LSIC. 

In recent years the Age Discrimination in Employment Act (ADEA) has been amended in 

ways that essentially outlaw mandatory retirement in the United States. In 1978, mandatory 

retirement below age 70 was banned. In 1986 it was banned altogether in almost all occupations. 

Mandatory retirement can therefore no longer serve as a device to support LSICs. In his original 

(1979) paper, Lazear argues against the legal ban on mandatory retirement for exactly this 

reason. 

The elimination of mandatory retirement does not make LSICs impossible. In principle, the 

LSIC could be structured to induce retirement at the efficient date by including a provision for a 

large wage cut at that date. This is probably not feasible, however; firms will not want to enter 

into such an arrangement since they know that it would be costly to actually make these wage 

cuts when the time came (possibly because workers cannot credibly promise not to agitate 

against these wage cuts when they arrive).10 LSICs can also be effectuated without mandatory 

retirement through a properly designed defined-benefit11 pension plan. Lazear (1983) shows that 

defined-benefit pension plans can be structured in such a way as to induce employees to retire at 

a specific date.12 The ban on mandatory retirement does, however, make LSICs more costly. 

Adjusting the terms of defined-benefit pension plans entails some costly deviation from the 

 

10 In principle, it is possible to design a self-enforcing LSIC without wage cuts, but it would involve inefficiently 

late retirement. Nevertheless, it might still be worthwhile in cases where LSICs are particularly valuable. 11 A defined-benefit pension is one in which a retiree’s pension payment (which continues until the retiree’s death), 

is determined by a formula that typically includes years of service and salary. A defined-contribution pension is 

essentially a savings plan to which the employee and (sometimes) the employer contribute. The key difference, for 

the purposes of this paper, is that defined-benefit plans influence retirement decisions much more directly than do 

defined-contribution plans. 

12 While that paper was concerned with early retirement (i.e., retirement before the agreed-upon mandatory 

retirement date), the same mechanism could, in principle, be used as a substitute for (now illegal) mandatory 

retirement. 
9

terms that had been (presumably) optimally chosen. Any major change in the accumulation of 

pension benefits would likely also necessitate (costly) changes in wages and other benefits. 

There is some evidence that defined-benefit pension plans have not been employed to 

“rescue” LSICs following the ban on mandatory retirement. In recent years there has been a 

shift away from defined-benefit pension plans altogether in favor of defined-contribution plans, 

which cannot support LSICs (Gustman and Steinmeier, 1992; Kruse, 1995; Ippolito, 1995). This 

trend is even more pronounced for new plans. Of plans established in 1995, only 3.2% of the 

plans (containing 10.6% of the participants) were defined-benefit (Private Pension Plan Bulletin, 

U.S. Department of Labor, 1999). This trend suggests (but does not prove) that defined-benefit 

pension plans are not being modified on a wide scale in order to support LSICs. 

B. Declining Benefits of LSICs 

LSICs tend to make workers less likely to quit their jobs. In order to induce a worker who is 

party to an LSIC to quit, an outside job offer would not only need to be superior, but superior by 

enough to induce the worker to forfeit the “bond” that he expects to recoup in the form of 

overpayment when old.13,14 LSICs also tend to make firms less likely to dismiss workers. In the 

absence of an LSIC the firm will dismiss any worker who is not productive enough to justify his 

compensation. But firms that are party to LSICs retain some workers that the firm would prefer 

to dismiss because dismissal might be perceived as a violation of the LSIC. Since LSICs prevent 

some (efficient) separations, they tend to lead to relatively long job matches. 

 

13 In an LSIC, the present discounted value of lifetime compensation (at the beginning of the contract) is equal to the 

present discounted value of MRP. But the underpayment in the early years of the contract is not fully repaid until 

the LSIC’s last day. Therefore at any point in an LSIC, the present discounted value of the contract’s remaining

compensation exceeds the present discounted value of the remaining MRP. 14 In his original (1979) paper Lazear shows that severance pay (i.e., returning the value of the unclaimed bond) can 

generate efficient separations. But severance pay is a limited phenomenon. It is certainly not the case that an 
10

Since LSICs tend to limit efficient separations, anything that makes efficient separations more 

likely (i.e., anything that makes workers or firms more likely to prefer to separate), reduces the 

benefits of LSICs and so makes them less likely to be formed. Efficient separations are more 

likely when workers find moving less distasteful, because alternative job offers are more likely 

to be perceived as superior. They are also more likely when firms have access to technologies 

and/or organizational structures that are more flexible and therefore rely less heavily on firmspecific human capital. It is widely believed that both of these changes have taken place in 

recent decades.15 

IV. Empirical Implications:

The purpose of this section is to develop empirically testable implications of the Hypothesis 

of this paper: that the formation of new LSICs between firms and young workers has declined or 

ceased.16 

Let ρ(t) represent the fraction of new entrants to the workforce at time t who enter into LSICs. 

Let td represent the date at which the net benefits of LSICs began to decline. Assume that ρ(t) 

 employee can simply demand the unpaid portion of an LSIC bond whenever he finds a better opportunity outside the 

firm. 

15 There is substantial evidence of a decline in job stability for males over the last few decades. (See Jaeger & 

Stevens (1999) and Neumark, Polsky & Hansen (1999), for evidence. See Stewart (1999) for a contrary view. Also 

see Bernhardt, Morris, Handcock & Scott (1999) for evidence specifically related to young males). This is 

consistent with an increase in worker mobility and/or a decrease in the importance of firm-specific human capital. 

The job stability literature is not directly on point, however, because it only reflects changes in the equilibrium 

frequency of job separations. This may be influenced by factors other than worker tastes and firm production 

processes. 

16 In an earlier version of the paper, I tested the Hypothesis using an approach based on the one used by Lazear & 

Moore (1984) to demonstrate that LSICs did in fact exist. In that paper, the authors develop a modified version of 

Lazear’s original theory so that it predicts a positive relationship between the slope of the age-earnings profile in an 

occupational category and lifetime earnings in that category. This positive relationship, if it exists, might be due to 

the presence of LSICs, or it might be the case that those occupational categories with steeper slopes have faster 

human capital accumulation, which leads to higher earnings. Lazear & Moore argue that data on self-employed 

workers can be used to purge the estimates (for wage and salary workers) of human capital accumulation effects 

since there are no incentive problems for self-employed workers. They argue that any remaining relationship 

between slope and lifetime earnings is due to LSICs. They used Current Population Survey (CPS) data from 1978. 
11

begins to decline at td and continues to decline thereafter as workers and firms gradually adapt to 

the new environment. This means that, for all t > td, there is an (increasing) number of “types” of 

workers who do not enter into LSICs, but who would have done so had they entered the 

workforce before td. These types of workers have a different structure of compensation 

depending on when they entered the labor force. 

This change in the structure of compensation for otherwise identical workers can generate 

testable empirical implications of the Hypothesis. If the rate of formation of LSICs among 

young workers entering the labor force is smaller after td (and decreasing after that), then the 

proportion of workers in the economy who are party to LSICs must also be smaller after td (and 

decreasing after that). Since LSICs tend to increase the returns to firm tenure, we can predict 

that average returns to firm tenure in the economy must decline after td. This prediction can be 

tested, with no additional assumptions beyond those listed above, as long as the sample period 

includes some years after td. The prediction can be stated as follows: 

Prediction 1: Returns to firm tenure declined after td. 

This is not a strong test, as there are a number of alternative reasons why returns to firm 

tenure might decline over time. But a stronger test can also be performed. If the Hypothesis 

were correct, we would not expect this decline in the returns to firm tenure to be the same for all 

levels of experience: specifically, we would expect the decline in the returns to firm tenure to be 

greater for low-experience workers. To see this, consider two dates: one shortly before td and 

one shortly after. The incidence of LSICs among high-tenure, high-experience workers will be 

 I repeated their test on CPS data from every year from 1978-1999. While the results (available upon request) were 

consistent with the Hypothesis, a variety of empirical problems cast doubt on the validity of this approach. 
12

the same on these two dates, while the incidence of LSICs for new entrants to the labor force 

(low-tenure, low-experience workers) will be smaller at the latter date. (The incidence of LSICs 

among low-tenure, high-experience workers is presumably low at both dates, as LSICs are not 

typically associated with older workers who start new jobs.) This means that, all else equal, the 

returns to firm tenure among high-experience workers (both high-tenure and low-tenure) should 

be the same on the two dates, whereas returns to firm tenure among low-experience workers 

should be lower in the later period.17 This prediction can be stated as follows: 

Prediction 2: The decline in the returns to firm tenure was greater for younger workers.

Note that testing Predictions 1 and 2 on a “full” sample of workers (including those who 

would not have been party to LSICs even before td) is conservative, as the predicted effects 

might be present but be attenuated by the presence of the other workers in the sample. 

V. Data and Summary Statistics:

The data for this study come from the Panel Study of Income Dynamics (PSID). The earliest 

plausible date for td is 1978 (the year that mandatory retirement below age 70 was banned), so 

the ideal sample would begin before 1978. Unfortunately, the PSID did not ask about job tenure 

 

17 As mentioned in footnote 4 above, a fundamental feature of an LSIC is that firms have an incentive to renege on 

their promise to overpay their older workers. It is typically assumed that reputation effects constrain such reneging: 

firms that acquire a reputation for reneging on LSICs will be unable to enter into them in the future. If firms are no 

longer entering into LSICs with young workers anyway, then they have less reason to refrain from reneging. To the 

extent that reneging exists (whether it takes the form of withholding promised wage increases or it takes the form of 

layoffs and other policies that induce separation), it tends to attenuate Prediction 2. But this effect on older workers, 

while likely present to some extent, probably does not cause Prediction 2 to be invalid. If the formation of LSICs 

with young workers has declined or ceased, then returns to tenure should have declined for a very large number of 

young workers (if they have ceased, then this number includes every young worker who would have been party to 

an LSIC had they entered the labor force before td). In contrast, the effect on older workers exists only for those 

workers whose firms chose to renege (for a variety of reasons, not all firms will find it in their interest to renege). 
13

in 1979 and 1980, so the sample begins in 1981 and runs through 1992. This sample restriction 

does not change the Predictions as long as the adjustment from the old equilibrium to the new 

one was not completed before the sample begins. 

The main sample consists of male “heads” of households who are PSID sample members18

and who are aged 25-54. Only these prime-age workers are included in order to avoid concerns 

related to the more erratic labor supply behavior of younger and older workers. Wages are 

deflated to 1976 dollars using the Consumer Price Index: All Urban Consumers (CPI-U). A 

person-year is included in the sample if the individual is a full-time (more than 1500 hours) 

worker in the private sector with hourly labor earnings greater than $1.50 who is not selfemployed. Government and unionized workers are excluded because they are difficult to fire; 

since LSICs are based on the threat of firing, these workers are unlikely to be party to them. 

Observations from the Survey of Economic Opportunity low-income over-sample are also 

excluded. Summary statistics are presented in Column 1 of Table 1. 

As discussed in the Appendix, the estimation approach that I use requires that the data for 

each individual be “partitioned” into different job matches. In other words, it is necessary to 

identify each job that an individual held during the sample period and to assign each observation 

to one of those jobs. It is not always obvious to which job match a particular observation 

belongs. Brown & Light (1992) show that biased coefficients on firm tenure (in wage equations 

using PSID data) can arise due to errors in assigning observations to job matches. They analyze 

a number of remedies for this, and recommend a partition (which they call partition “T”) that 

 

18 Not everyone in a PSID household is a sample member. The PSID collects data on all members of the household 

in which a sample member lives, which includes people who are not sample members. I only use data on sample 

members because the presence of non-members in the dataset depends on things like marriage and divorce, which 

can bias the sample. 
14

assumes that a job change has occurred whenever reported tenure is less than the elapsed time 

since the previous interview. I follow their recommendation and use partition T.19

VI. Estimation and Main Results:

A. Estimation 

To test Predictions 1 and 2, I estimate the following wage equation: 

i ij ijt

ijt it ijt t it t ijt it t

ijt ijt ijt it it t t

X

TEN EXP TEN TIME EXP TIME TEN EXP TIME

wage TEN TEN EXP EXP TIME TIME

α θ ε

β β β β

β β β β β β

Γ + + +

⋅ + ⋅ + ⋅ + ⋅ ⋅ +

= + + + + + +

7 8 9 10

2

5 6

2

3 4

2

1 2 (1) ln

 

TEN is firm tenure, EXP is potential work experience (age-education-6),20 TIME is a linear 

time trend, and X is a vector of standard control variables including four education dummies, a 

non-white dummy, an ever-married dummy, four region dummies, and 2-digit 1970 census 

industry and occupation dummies.21 α and θ are individual specific and job-match specific 

effects, respectively, and ε is a noise term. The subscripts i, j, and t index the individual, the job 

match, and time, respectively. The reason for indexing the job will be made clear in the 

estimation section below. 

Prediction 1 is that the effect of firm tenure on wages is declining over time, or: 

 

19 Brown & Light also show that the tenure variable in the PSID must be “cleaned” in order for it to produce correct 

results. Specifically, it must be modified so that it is forced to increase at the same rate as the time variable (which 

is often not the case in the raw data). They evaluate a number of ways of doing this, and they find that all perform 

about equally well. I take the approach of assuming that reported tenure for the first year of each job match is 

correct, and then forcing tenure to increase by one year for each year of the job match. The potential experience 

variable is cleaned in a similar manner. 

20 The PSID is not consistent in its questions about the number of years of post-graduate schooling. In order to use 

the full sample, I assign 16 years of schooling to someone whose highest education is an undergraduate degree, and 

19 years to someone who reports a post-graduate degree. 
15

0 ln

8 10

2

= + <

∂ ⋅ ∂

∂ EXP

TEN TIME

wage β β

Prediction 2 is that this effect is less pronounced for more experienced workers, or: 

0 ln

10

3

= >

∂ ⋅ ∂ ⋅ ∂

∂ β TEN TIME EXP

wage

There is considerable controversy regarding the correct method for estimating returns to firm 

tenure (Abraham & Farber, 1987; Altonji & Shokotko, 1987; Topel, 1991). I follow Parent 

(2000) and use the instrumental variables (IV) approach of Altonji & Shokotko (described in the 

Appendix). Since the PSID follows individuals over time, there is an individual-specific 

component in the error term. In order to get the correct standard errors, a cluster correction is 

performed. 

 

B. Main Results 

Full-sample IV regression results for three different specifications of the wage equation are 

reported in Table 2. Two sets of results are reported for each specification: one that includes 

dummies for the two-digit 1970 census industries and occupations, and one that does not. For 

comparison purposes, OLS results (for Specification 3 only) are reported as well. In this section 

I will discuss the Specification 3 IV results (with industry and occupation dummies included), 

which are the most important since Specification 3 corresponds to Equation 1 above. 

 21 Industries and occupations with fewer than 25 observations are omitted. 
16

The IV estimate of β8 is -.0081 and IV estimate of β10 is equal to .00035. Both of these 

coefficients are significant at the 1% level. These results confirm Prediction 1: returns to firm 

tenure have decreased on average.22 They also confirm Prediction 2: the decline in returns to 

firm tenure is large for low-experience workers, but not for high-experience workers. 

VII. Alternative Explanations and Additional Results:

As discussed above, the main empirical analysis confirms that tenure-earnings profiles have 

changed according to Predictions 1 and 2. But this does not necessarily constitute confirmation 

of the Hypothesis; it remains possible that the Hypothesis is false but that some other 

phenomenon has caused Predictions 1 and 2 to be confirmed. The purpose of this section is to 

examine the extent to which it is possible to be confident that the changes in tenure-earnings 

profile discussed above are due to the hypothesized cause rather than to some alternative one. 

A. Alternative Explanations 

The most obvious candidates for alternative explanations include changes in technology and 

in worker tastes for mobility. I consider each of these in turn: 

1. Technology:

Suppose that technology changes at time ttech so that firm-specific human capital is less 

valuable than it was before. For simplicity and without loss of generality, assume that only low-

 

22 The functional form of Equation 1 imposes a linear relationship between the change in returns to tenure and 

experience, which means that, as long as β10 > 0, the change in returns to tenure will eventually become positive. 

Since this relationship is positive only at high levels of potential experience, we can be confident that, on average, 

the change is negative. Another way to see this is to look at Specification 2, where the IV estimate of the coefficient 

on the interaction term between the time trend and the tenure variable is equal to -.0047, which is significant at the 

5% level. 
17

experience workers invest in firm-specific human capital and that the level of investment 

depends on the state of technology. Those workers who entered the work force after time ttech

would have less firm-specific human capital (due to the reduced investment) and also a lower 

return (due to the technology change). Those workers who entered the work force before time 

ttech, on the other hand, would have an unchanged level of firm-specific human capital, but would 

still have a lower return. This technology-based explanation would generate both Prediction 1 

(all workers have a decline in returns to firm tenure), and Prediction 2 (the decline in returns to 

firm tenure is larger for low-experience workers). 

If the technology change were small, then the change in the average return to firm tenure 

would also be small. As can be seen in Specification 2, this is not the case: the coefficient on the 

interaction between firm tenure and the time trend is equal to -.0047, and is significant at the 5% 

level. In order for the technology explanation to be correct, the technology change during the 

sample period would have had to be substantial. 

2. Tastes:

Suppose that tastes changes at time ttaste so that low-experience workers object to changing 

jobs less than they had in the past. For simplicity and without loss of generality, assume that 

only low-experience workers invest in firm-specific human capital and that the level of 

investment depends on the worker’s tastes (workers who believe that a preferred job opportunity 

is likely to come along invest less in firm-specific human capital). Those workers who entered 

the work force after time ttaste would have less firm-specific human capital (due to the reduced 

investment) and an unchanged return. Those workers who entered the work force before time 

ttaste, on the other hand, would have an unchanged level of firm-specific human capital and an 
18

unchanged return. This taste-based explanation would generate both Prediction 1 (on average, 

workers have a decline in returns to firm tenure), and Prediction 2 (the decline in returns to firm 

tenure is larger for low-tenure workers). 

In order for this alternative explanation to be correct, it would have to be the case that this 

change in tastes was both substantial and that it took place within the relatively brief sample 

period. 

In sum, there are plausible arguments in favor of alternative explanations for the fact that 

Propositions 1 and 2 were confirmed in the main empirical analysis. For this reason, I perform 

some additional tests that are intended to distinguish between these explanations. 

B. Additional Tests 

 In order to further explore the question of whether the main results have some cause other 

than the hypothesized one, I perform the following exercise: I identify other groups in the data 

(both sub-groups of the full (male) sample and a separate female sample); determine whether or 

not, according to the Hypothesis, Predictions 1 and 2 are expected to hold for those groups; and 

then perform empirical tests similar to those performed on the main sample to see how well the 

Hypothesis holds up. 

1. “White-Collar” and “Not White-Collar” Sub-Samples 

To perform this test, I split the main (male) sample into two sub-samples. Ideally, one subsample would be defined as workers who would have entered into LSICs had they entered the 

workforce before td (Category A), and the other sub-sample would be defined as workers who 
19

would not have done so (Category B). This division would permit the following test: if the 

Hypothesis were correct, we would expect Predictions 1 and 2 to hold for the Category A subsample, but not for the Category B sub-sample, because only in the Category A sub-sample is 

there a decline in the proportion of workers who are entering into LSICs: in the Category B subsample, no LSICs were being formed in the first place. As a practical matter, however, any 

actual division of the sample will only approximate this ideal division. This means that both 

sub-samples will contain some workers who would properly be assigned to Category A. For this 

reason, we would expect that Predictions 1 and 2 would hold to some extent for both subsamples, but would hold more strongly for the one that is intended to approximate Category A. 

To understand what it means for the Predictions to hold “more strongly” for one sub-sample 

than for the other, consider the following thought experiment: suppose we had a perfect 

mechanism for assigning workers to the correct category. In this case, we would expect the 

following results (if the Hypothesis is correct): (i) there would be no change in the returns to 

tenure for workers in Category B, regardless of the level of experience; and (ii) there would be a 

decline in the returns to tenure for low-experience workers in Category A, but this decline would 

approach zero at high levels of experience. Since the functional form of Equation 1 imposes a 

linear relationship between the change in returns to tenure and experience, this corresponds to a 

decline in returns to tenure for low-experience workers, and an increase in returns to tenure for 

workers with sufficiently high experience. Figure 1 plots the predicted relationship between the 

change in returns to tenure and experience for the two “perfect” Categories.23 If the estimation 

produced results such as those in Figure 1, we would say that the Hypothesis holds for Category 

A, but not for Category B. In practice, the imperfect method for assigning workers to categories 
20

means that both sub-samples will contain some workers who would properly be assigned to 

Category A. This means that we would expect the Category B line to be upward sloping, but to 

start higher and have a flatter slope than the Category A line. See Figure 2 for an illustration. If 

the estimation produced results such as those in Figure 2, we would say that the Hypothesis 

holds more strongly for Category A than for Category B 

In order to split the sample in the manner described above, it is necessary to find some 

information that can serve as a proxy for a worker’s propensity to be party to an LSIC. The most 

plausible proxy is a property of occupations, not of individuals. Specifically, LSICs are 

generally thought to be more likely to arise in occupations in which employee monitoring is 

difficult. I treat all workers in occupations in which monitoring is “difficult” as belonging to 

Category A and all workers in occupations in which monitoring is not difficult as belonging to 

Category B. The idea behind this is that workers in occupations in which monitoring is not 

difficult were unlikely to be party to LSICs either before or after td, whereas the percentage of 

workers who are in difficult-to-monitor occupations and who are party to LSICs should be 

decreasing after td.

24 

To divide the occupations into those that are and are not difficult to monitor, I follow Valletta 

(1999) and assume that employee monitoring is difficult in “white-collar” occupations, but not in 

other occupations. I define a worker as white-collar if he is in a 1970 2-digit census occupation 

of 30 or less. These occupations are titled “Managerial and Professional Specialty 

 23 Any changes in returns to tenure (for either group) that are due to changes in the accumulation of or returns to 

firm-specific human capital that are not related to experience would show up as parallel shifts in these lines. As 

long as these shifts are not too large, the definition of the Predictions holding “more strongly” holds. 

24 This is certainly true if the distribution of workers across occupations does not change after the decline or demise 

of LSICs. If, on the other hand, the decline or demise of LSICs causes a the share of workers employed in “difficult 

to monitor” occupations to fall, then a decline in the formation of LSICs with young workers does not necessarily 

reduce the fraction of young workers who are party to LSICs. 
21

Occupations.”25 I omit job matches in which the worker’s white-collar/not-white-collar status 

changes within the match. For this reason, the sum of the sample sizes of the two sub-samples is 

smaller than the sample size of the main sample (4802 vs. 6395). Summary statistics for the two 

sub-samples are presented in Columns 2 and 3 of Table 1. 

Regression results for the white-collar and not-white-collar sub-samples are reported in 

Tables 3a and 3b. Two sets of results are reported for each specification: one that includes 

dummies for the two-digit 1970 census industries, and one that does not. Occupation dummies 

are omitted because the white-collar and not-white-collar categories are themselves derived from 

the occupation variable. For the white-collar sub-sample, the IV estimate of β8 is equal to -.0047 

in the specification that controls for industry, and is equal to -.0063 in the specification that does 

not. Neither of these coefficients is significant at conventional levels. The IV estimate of β10 is 

equal to .00024 in the specification that controls for industry, and is equal to .00033 in the 

specification that does not. Neither of these coefficients is significant at conventional levels. 

For the not-white-collar sub-sample, the IV estimate of β8 is equal to -.0120 in the 

specification that controls for industry, and is equal to -.0133 in the specification that does not. 

Both of these coefficients are significant at the 1% level. The IV estimate of the coefficient β10

is equal to .00046 in the specification that controls for industry, and is equal to .00048 in the 

specification that does not. Both of these coefficients are significant at the 1% level. 

 

25 I performed a similar exercise by splitting the sample between high and low levels of the “substantive 

complexity” of an occupation. The substantive complexity variable is a weighted average of a number of 

occupational characteristics as measured by the Dictionary of Occupational Titles (DOT). The DOT defines about 

14,000 occupations and rates each one according to a large number of such characteristics. That is, it assigns each 

occupation a score for each characteristic. I follow Roos & Tremain (1980) in converting these scores from the DOT 

occupations to the 1970 3-digit census occupations. There are many more DOT occupations than there are 3-digit 

census occupations. The score for a given census occupation for each characteristic is the employment-weighted 

average of the scores of the constituent DOT occupations. An approach along these lines was first used by Hutchens 

(1987) to test for the existence of LSICs. The results for this sample division are similar to those for the “whitecollar” vs. “not-white-collar” division and so are not reported. 
22

These coefficients all have the correct signs (though they are statistically insignificant in the 

white-collar sub-sample). Both sub-samples exhibit a decline in the returns to firm tenure for 

low-experience workers, and the magnitude of this decline diminishes with experience for both 

sub-samples. However, contrary to what we would expect if the Hypothesis were correct, these 

results hold more strongly for the not-white-collar sub-group. These results are illustrated in 

Figures 3a (based on the specification that includes industry dummies) and in Figure 3b (based 

on the specification that does not). 

 The split-sample results do not support the Hypothesis (because the results were stronger for 

the not-white-collar sub-sample), but do not strongly refute it either (because both sets of 

coefficients had the correct signs). This lack of support may mean that they Hypothesis is false, 

or it may be a reflection of the weaknesses of the split-sample approach. These weaknesses 

include: (i) the crudeness of the method used to determine who is likely to be party to an LSIC,26

(ii) the imprecision of the regression results (particularly for the white-collar sub-sample), and 

(iii) the possibility that the two sub-groups had different experiences in terms of the 

accumulation of and/or returns to human capital during the sample period. 

2. Female Sample 

To perform this test, I use a sample of female workers. The female sample is defined in the 

same way as the male sample, except that it consists of female PSID household heads and of 

female “wives” who are married to male household heads. Summary statistics are presented in 

Column 4 of Table 1. 

 26 Monitoring difficulty is only one factor that influences the likelihood that an LSIC will form. Other factors 

include the size and age of the firm (workers will only make implicit contracts with firms that are expected to be 

around decades in the future to honor them), the degree of intrinsic motivation of the employees (which might be 
23

As discussed above, LSICs are typically not associated with women. Therefore, confirmation 

of Predictions 1 and 2 for men, but not for women, would tend to support the Hypothesis. On the 

other hand, confirmation of Predictions 1 and 2 for women as well as men would suggest that the 

effects found above for men had some alternative cause that was common to men and women.27 

Regression results for the female sample are reported in Table 4. The PSID did not ask about 

the race of wives until 1985. For this reason, the non-white dummy is omitted from the female 

sample regressions. The IV estimate of β8 is equal to -.0075 in the specification that controls for 

industry and occupation, and is equal to -.0051 in the specification that does not. The former 

coefficient is significant at the 5% level; the latter coefficient is not significant at conventional 

levels. The IV estimate of β10 is equal to .00023 in the specification that controls for industry 

and occupation, and is equal to .00015 in the specification that does not. The former coefficient 

is significant at the 10% level; the latter coefficient is not significant at conventional levels. 

If the Hypothesis is correct, the Predictions should hold for the main (male) sample, but not 

for the female sample; at least, the results should hold more strongly for the main sample. The 

Predictions do hold more strongly for the main sample than for the female sample, but the 

strength of this result depends on the specification. Specifically, the result holds much more 

strongly for the specification that excludes the industry and occupation dummies; for the 

specification that includes industry and occupation dummies, the coefficients for the female 

sample are not significant at conventional levels. This may be because the difference between 

male and female tenure-earnings profiles is largely due to differences in their rates of 

participation in different industries and occupations: once these are controlled for, the differences 

 higher in “better” jobs), and the complementarity between supervision and work (if the normal course of production 

involves close proximity between employee and supervisor then LSICs are less likely to be formed). 27 In principle, the possibility would remain that similar effects for the two genders had different causes, but this 

seems unlikely. 
24

may be much smaller. For this reason, the specification that omits the industry and occupation 

dummies may be the preferred one. These results are illustrated in Figures 4a (based on the 

specification that includes industry dummies) and in Figure 4b (based on the specification that 

does not).28 

VIII. Conclusions:

In this paper I develop the hypothesis that the formation of LSICs between firms and young 

workers has declined or ceased. I develop two testable Predictions of this Hypothesis (involving 

changes in the returns to firm tenure), and I test them on PSID data using the instrumental 

variables method developed by Altonji & Shokotko (1987). The Predictions are confirmed in the 

full-sample results. I discuss two possible alternative scenarios in which the Predictions could be 

confirmed even if the Hypothesis were false. In order to differentiate between these competing 

explanations, I conduct additional tests. The split-sample results (in which the sample is divided 

into occupations that are more and less amenable to the formation of LSICs) do not support the 

Hypothesis, but do not strongly refute it either. It is unclear whether this lack of support 

indicates that the Hypothesis is false, or if it is merely a reflection of weaknesses in the splitsample approach. The female sample results support the Hypothesis, but the strength of this 

support depends on the regression specification. The mixed nature of these results leads me to 

conclude that the Hypothesis may be true, but that more research is necessary to reach a firm 

conclusion. 

This paper makes no attempt to determine the relative contributions of legal and economic 

factors in the decline or demise of LSICs. Perhaps there would have been no decline had it not 

 

28 In order make the samples comparable, I repeated the estimation for the main sample omitting the “nonwhite” 

variable. The estimates change very little (compared to those in Table 2) so they are not reported. But the line for 
25

been for the legal ban on mandatory retirement. On the other hand, perhaps the benefits of 

LSICs were declining to the point that they would have eventually ceased to be formed even if 

mandatory retirement had remained legal. It is unclear whether the ban on mandatory retirement 

is the primary cause for the decline of LSICs, or if it is merely the trigger. 

If in fact LSICs are no longer being formed with younger workers, a number of interesting 

things may come about as a result. It is widely believed that in the 1990s the U.S. entered a 

period of widespread corporate “downsizing” in which firms laid off workers -- often welleducated workers with high tenure -- to an extent far greater than in the past. Recent empirical 

studies have lent some support to this belief. This phenomenon may be due in part to the decline 

of LSICs for younger workers. If firms are no longer forming LSICs with young workers, then 

they have a diminished incentive to protect their reputations for trustworthiness and therefore 

have a greater incentive to renege on their obligations to older workers with LSICs by laying 

them off. I develop and test this argument in another paper (Balan, 2002). 

The demise of LSICs will tend to cause heavier reliance on alternative mechanisms for 

dealing with principal-agent problems. This may include more monitoring. It may also include 

heavier reliance on efficiency wages. In future research, I plan to investigate whether or not 

efficiency wages became more heavily used as LSICs declined. 

 the main sample in Figures 4a and 4b are based on the specification that omits the nonwhite variable. 
26

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29

Appendix: 

Altonji & Shokotko (1987) Approach to Estimating the Returns to 

Firm Tenure 

Altonji & Shokotko (AS) consider a wage regression like the following (dropping the square 

terms for ease of presentation): 

ijt TENijt EXPit X i ij ijt A lwage = β + β + Γ +α +θ + ε 1 2 ( 1)

In this specification, the error term is decomposed into a person-specific fixed effect (ability) 

αi, a job-specific fixed effect (match quality) θij and a noise term εijt. The problem in estimating 

β1 is that tenure is likely correlated with the error term. It is likely to be positively correlated 

with αi because high ability individuals or individuals with fewer health or personal problems are 

likely to have higher wages and lower turnover/higher tenure. It is also likely to be correlated 

with θij because workers who are getting high wages relative to their alternatives (which is more 

likely when θij is high) are not likely to quit. Similarly, firms that are getting some surplus from 

a worker (which is also more likely when θij is high) are not likely to lay him off. Both of these 

effects suggest a positive correlation between the tenure variables and θij. On the other hand, 

workers only quit if their alternative is better than their current job. A job with a very good 

match will tend to be negatively correlated with tenure to the extent that the better job enticed the 

worker to switch (and thereby lowered his tenure). AS maintain that overall the correlation 

between the tenure variables and θij (and hence the correlation between the tenure variables and 

the error term) is positive. 

AS propose an instrumental variables approach to dealing with this problem. The principal 

instrument for the tenure variable is the deviation from the mean tenure for a job match. The 

tenure instrument is uncorrelated by construction with αi. To see this, imagine two individuals, 
30

one of high ability and the other of low ability. The high-ability individual will tend to have high 

tenure and the low-ability individual will tend to have low tenure. But neither is more likely than 

the other to have a high value of the tenure instrument because for every individual, every value 

that the instrument takes on is balanced by another value of the same magnitude and the opposite 

sign. A similar argument demonstrates that tenure is uncorrelated with θij. The tenure 

instrument is correlated with tenure. Within a given job, higher values of tenure always 

correspond to higher values of the instrument. 

Experience might also be correlated with the error term. People with a higher value for the 

individual fixed effect αi might have fewer or shorter unemployment spells and hence more 

experience. People with more experience might have a higher value for the job match specific 

error component θij since they have had more time to search for a good job match. But this is not 

an issue when potential experience is used. 
Variable

Full Sample "White-Collar" 

Sub-Sample

"Not WhiteCollar" SubSample

Female 

Sample

Age (years) 36.42 37.53 35.58 37.20

(7.84) (7.82) (8.19) (8.41)

Percent Education < 12 .109 .016 .224 .102

Percent Education = 12 .318 .128 .477 .468

Percent Education = 13-15 .223 .200 .184 .254

Percent Education >=16 .351 .657 .116 .175

Percent Nonwhite .061 .053 .093 N/A

Percent Ever Married .884 .869 .893 .850

Percent in Northeast .199 .266 .150 .218

Percent in North Central .300 .301 .287 .247

Percent in South .329 .263 .396 .338

Percent in West .171 .169 .168 .197

Firm Tenure 7.10 7.89 5.56 5.83

(7.04) (7.82) (6.32) (5.72)

Potential Experience 16.80 16.29 17.37 18.26

(8.30) (8.09) (8.80) (9.00)

Annual Hours Worked 2322 2348 2283 2053

(424) (418) (444) (353)

Hourly Labor Earnings (1976 dollars) 7.96 10.64 5.53 5.00

(5.36) (6.78) (3.01) (2.81)

Number of Individuals 1193 464 663 1048

Number of Observations 6395 2248 2554 4381

Notes:

a. The full sample, the "white-collar" sub-sample, and the "not white-collar" sub-sample consist of male household "heads" who

 are PSID sample members between 25 and 54 years of age who have full-time, private-sector, non-union jobs and who are 

 not self-employed. The "female" sample is similar but consists of female household heads and of "wives."

b. Standard deviations in parentheses.

c. Note that the sum of the observations in the male sub-samples is less than the number of observations in the full male sample.

 This is because the sub-samples do not include matches where the status changes within the match.

 

Table 1: Descriptive Statistics
Without 

Industry and 

Occupation 

Dummies

With Industry 

and 

Occupation 

Dummies

Without 

Industry and 

Occupation 

Dummies

With Industry 

and 

Occupation 

Dummies

Variable IV Estimates IV Estimates IV Estimates IV Estimates OLS Estimates IV Estimates OLS Estimates 

Tenure .0331*** .0250*** .0350*** .0292*** .0287*** .1397*** .0268*** 

(.0043) (.0042) (.0055) (.0055) (.0077) (.0306) (.0067) 

Tenure squared -.0010*** -.0005** .0027*** .0022** -.0009*** .0016** -.0008*** 

(.0002) (.0002) (.0010) (.0011) (.0003) (.0008) (.0002) 

Potential Experience .0203*** .0210*** .0308*** .0298*** .0245*** .0293*** .0244*** 

(.0063) (.0053) (.0091) (.0084) (.0058) (.0092) (.0052) 

Potential Experience squared -.0002 -.0004** -.0012*** -.0011*** -.0004*** .0002 -.0004*** 

(.0002) (.0001) (.0004) (.0004) (.0002) (.0003) (.0001) 

Time Trend -.0181*** -.0136** .0420* .0288 -.0077 .0393** -.0058 

(.0069) (.0063) (.0216) (.0217) (.0091) (.0156) (.0082) 

Time Trend squared .0012** .0009** .0005 .0005 .0011** .0018*** .0009* 

(.0005) (.0005) (.0007) (.0006) (.0005) (.0006) (.0005) 

Tenure*Potential Experience .0005 -.0059*** .0003 

(.0004) (.0016) (.0004) 

Tenure*Time Trend -.0062*** -.0047** -2.71E-05 -.0113*** -.0004 

(.0020) (.0022) (.0011) (.0032) (.0009) 

Potential Experience*Time trend -.0005 -.0029*** -.0005 

(.0004) (.0011) (.0004) 

Tenure*Time Trend*Potential Experience .00001 .00046*** .00003 

(.00004) (.00014) (.00004) 

R-Squared .3698 .5144 .0074 .2681 .3900 .3032 .5169 Number of Observations 6395 6395 6395 6395 6395 6395 6395 

Notes:a. Dependent variable is log of hourly labor earnings.b. IV results are reported for Specifications 1 - 3. OLS results are reported for Specification 3 only. c. The full sample consists of male household "heads" who are PSID sample members between 25 and 54 years of age who have full-time, private sector, non-union jobs and who are not  self-employed.d. Unreported regressors include four education dummies, a nonwhite dummy, an ever-married dummy, and four region dummies. The "With Industry and Occupation Dummies" Specifications  also include 2-digit 1970 census industry and occupation dummies.e. Standard errors are in parentheses.f. A clustering correction is made allowing the error terms for the observations of an individual to be correlated.g. One, two, and three stars indicate significance at the 10, 5, and 1 percent levels, respectively.

(3)

Without Industry and 

Occupation Dummies

With Indus

Occupation 

Table 2. Full Sample Wage Equation Estimates

(1) (2)
Without 

Industry 

Dummies

With Industry 

Dummies

Without 

Industry 

Dummies

With Industry 

Dummies

Variable IV Estimates IV Estimates IV Estimates IV Estimates OLS Estimates IV Estimates OLS Estimates IV 

Tenure .0269*** .0236*** .0204** .0197** .0218* .1032** .0245 

(.0080) (.0079) (.0099) (.0098) (.0124) (.0497) (.0122) 

Tenure squared -.0007* -.0004 .0017 .0016 -.0003 .0007 -.0002 

(.0004) (.0004) (.0016) (.0019) (.0005) (.0012) (.0004) 

Potential Experience .0211** .0289*** .0500** .0521** .0244** .0205 .0302*** 

(.0106) (.0102) (.0237) (.0257) (.0104) (.0143) (.0103) 

Potential Experience squared -.0001 -.0004 -.0016 -.0016 -.0004 .0009 -.0004 

(.0004) (.0004) (.0010) (.0011) (.0003) (.0008) (.0003) 

Time Trend .0094 .0124 .0551 .0511 .0091 .0569* .0142 

(.0104) (.0101) (.0378) (.0419) (.0151) (.0318) (.0145) 

Time Trend squared -.0003 -.0005 -.0013 -.0012 -.0003 .0001 -.0004 

(.0008) (.0007) (.0011) (.0011) (.0008) (.0010) (.0007) 

Tenure*Potential Experience .0001 -.0049* -.0002 

(.0008) (.0028) (.0008) 

Tenure*Time Trend -.0033 -.0030 .0007 -.0063 .0002 

(.0026) (.0031) (.0018) (.0046) (.0018) 

Potential Experience*Time trend 3.48E-05 -.0033 -.0002 

(.0009) (.0028) (.0008) 

Tenure*Time Trend*Potential Experience -.00002 .00033 -7.49E-07 

(.00008) (.00024) (.00008) 

R-Squared .2484 .3312 .0591 .1548 .2610 .1921 .3336 Number of Observations 2248 2248 2248 2248 2248 2248 2248 

Notes:a. Dependent variable is log of hourly labor earnings.b. IV results are reported for Specifications 1 - 3. OLS results are reported for Specification 3 only. c. The "white-collar" sample consists of male household "heads" who are PSID sample members between 25 and 54 years of age who have full-time, private sector, non-union jobs, who are not self-employed, and who work in "Managerial and Professional Specialty Occupations."d. Unreported regressors include four education dummies, a nonwhite dummy, an ever-married dummy, and four region dummies. The "With Industry Dummies" Specifications  also include 2-digit 1970 census industry dummies.e. Standard errors are in parentheses.f. A clustering correction is made allowing the error terms for the observations of an individual to be correlated.g. One, two, and three stars indicate significance at the 10, 5, and 1 percent levels, respectively.

(3)

Without Industry 

Dummies With Industry D

Table 3a. Wage Equation Estimates for the "White-Collar" Sub-Sample

(1) (2)
Without 

Indsutry 

Dummies

With Industry 

Dummies

Without 

Indsutry 

Dummies

With Industry 

Dummies

Variable IV Estimates IV Estimates IV Estimates IV Estimates OLS Estimates IV Estimates OLS Estimates IV 

Tenure .0414*** .0404*** .0459*** .0475*** .0253** .1617*** .0310*** 

(.0070) (.0070) (.0086) (.0088) (.0127) (.0472) (.0115) 

Tenure squared -.0016*** -.0013*** .0015 .0011 -.0014*** .0011 -.0014*** 

(.0003) (.0003) (.0010) (.0010) (.0004) (.0014) (.0004) 

Potential Experience .0020 .0068 .0077 .0096 .0098 .0153 .0119 

(.0087) (.0074) (.0101) (.0088) (.0085) (.0135) (.0076) 

Potential Experience squared .0001 -.0001 -.0004 -.0004 -.0002 .0003 -.0003 

(.0002) (.0002) (.0003) (.0003) (.0002) (.0003) (.0002) 

Time Trend -.0332*** -.0358*** .0008 -.0093 -.0203 .0156 -.0266** 

(.0117) (.0111) (.0200) (.0188) (.0136) (.0207) (.0129) 

Time Trend squared .0023*** .0024*** .0019* .0022 .0020** .0033*** .0022*** 

(.0009) (.0008) (.0010) (.0009) (.0008) (.0010) (.0008) 

Tenure*Potential Experience .0009 -.0057** .0007 

(.0006) (.0022) (.0005) 

Tenure*Time Trend -.0048** -.0040** .0002 -.0133*** -.0001 -

(.0019) (.0020) (.0018) (.0047) (.0016) 

Potential Experience*Time trend -.0006 -.0025* -.0004 

(.0005) (.0013) (.0005) 

Tenure*Time Trend*Potential Experience .00001 .00048*** .00001 .

(.00007) (.00018) (.00006) 

R-Squared .2214 .3493 .0197 .1805 .2646 .1370 .3639 Number of Observations 2554 2554 2554 2554 2554 2554 2554 

Notes:a. Dependent variable is log of hourly labor earnings.b. IV results are reported for Specifications 1 - 3. OLS results are reported for Specification 3 only. c. The "not white-collar" sample consists of male household "heads" who are PSID sample members between 25 and 54 years of age who have full-time, private sector, non-union jobs, who are not self-employed, and who do not work in "Managerial and Professional Specialty Occupations."d. Unreported regressors include four education dummies, a nonwhite dummy, an ever-married dummy, and four region dummies. The "With Industry Dummies" Specifications  also include 2-digit 1970 census industry dummies.e. Standard errors are in parentheses.f. A clustering correction is made allowing the error terms for the observations of an individual to be correlated.g. One, two, and three stars indicate significance at the 10, 5, and 1 percent levels, respectively.

Table 3b. Wage Equation Estimates for the "Not White-Collar" Sub-Sample

(3)

Without Industry 

Dummies With Industry D
Without 

Industry and 

Occupation 

Dummies

With Industry 

and 

Occupation 

Dummies

Without 

Industry and 

Occupation 

Dummies

With Industry 

and 

Occupation 

Dummies

Variable IV Estimates IV Estimates IV Estimates IV Estimates OLS Estimates IV Estimates OLS Estimates IV 

Tenure .0385*** .0361*** .0428*** .0418*** .0485*** .0770* .0503*** 

(.0052) (.0049) (.0065) (.0058) (.0105) (.0416) (.0096) 

Tenure squared -.0009*** -.0010*** .0006 .0009 -.0004 .0005 -.0004 

(.0003) (.0003) (.0007) (.0006) (.0003) (.0008) (.0003) 

Potential Experience .0185*** .0194*** .0162*** .0173*** .0229*** .0190** .0231*** 

(.0055) (.0045) (.0057) (.0048) (.0056) (.0086) (.0048) 

Potential Experience squared -.0004*** -.0004*** -.0005*** -.0005*** -.0004*** -.0003 -.0004*** 

(.0001) (.0001) (.0001) (.0001) (.0001) (.0003) (.0001) 

Time Trend -.0103 -.0052 -.0013 .0061 .0026 .0105 .0070 

(.0083) (.0076) (.0100) (.0093) (.0097) (.0172) (.0090) 

Time Trend squared .0014** .0011** .0017** .0015** .0014** .0017** .0011** 

(.0006) (.0005) (.0007) (.0006) (.0006) (.0007) (.0005) 

Tenure*Potential Experience -.0009* -.0018 -.0011*** 

(.0005) (.0019) (.0004) 

Tenure*Time Trend -.0026* -.0034*** -.0012 -.0051 -.0019* 

(.0015) (.0013) (.0012) (.0043) (.0011) 

Potential Experience*Time trend -.0009** -.0008 -.0008** 

(.0004) (.0009) (.0003) 

Tenure*Time Trend*Potential Experience .00008* .00015 .00010* 

(.00005) (.00017) (.00004) 

R-Squared .2980 .4549 .2618 .4002 .3037 .2812 .4619 Number of Observations 4381 4381 4381 4381 4381 4381 4381 

Notes:a. Dependent variable is log of hourly labor earnings.b. IV results are reported for Specifications 1 - 3. OLS results are reported for Specification 3 only. c. The full sample consists of female household "heads" and "wives" who are PSID sample members between 25 and 54 years of age who have full-time, private sector, non-union jobs and who  are not self-employed.d. Unreported regressors include four education dummies, an ever-married dummy, and four region dummies. The "With Industry and Occupation Dummies" Specifications  also include 2-digit 1970 census industry and occupation dummies.e. Standard errors are in parentheses.f. A clustering correction is made allowing the error terms for the observations of an individual to be correlated.g. One, two, and three stars indicate significance at the 10, 5, and 1 percent levels, respectively.

Table 4. Female Sample Wage Equation Estimates

(3)

Without Industry and 

Occupation Dummies

With Industr

Occupation Du

(1) (2)
Figure 1--Predicted Effects for "Perfect" Categories

-.0140-.0120-.0100-.0080-.0060-.0040-.0020

.0000.0020.0040

1 5 9 13 17 21 25 29

Potential Experience

Change in Return to Firm Tenure Category A Category B

Figure 2--Predicted Effects for "Imperfect" C

-.0140-.0120-.0100-.0080-.0060-.0040-.0020

.0000.0020.0040

1 5 9 13 17 21 

Potential Experience

Change in Return to Firm Tenure 

Figure 3a--White Collar vs. Not-White Collar

(based on regressions with industry controls)

-.0140-.0120-.0100-.0080-.0060-.0040-.0020

.0000.0020.0040

1 5 9 13 17 21 25 29

Potential Experience

Change in Return to Firm Tenure White-Collar Not-White-Collar

Figure 3b--White Collar vs. Not-White C

(based on regressions without industry co

-.0140-.0120-.0100-.0080-.0060-.0040-.0020

.0000.0020.0040

1 5 9 13 17 21 25 

Potential Experience

Change in Return to Firm Tenure 
Figure 4a--Main (male) vs. Female

(based on regressions with industry and occupation controls)

-.0140-.0120-.0100-.0080-.0060-.0040-.0020

.0000.0020.0040

1 5 9 13 17 21 25 29

Potential Experience

Change in Return to Firm Tenure Main (male) Female

Figure 4b--Main (male) vs. Female

(based on regressions without industry and occup

-.0140-.0120-.0100-.0080-.0060-.0040-.0020

.0000.0020.0040

1 5 9 13 17 21 

Potential Experience

Change in Return to Firm Tenure