Working Hard For The Money? Eciency Wages And Worker


Working hard for the money? Eciency wages and worker


Arthur H. Goldsmith a,*, Jonathan R. Veum b,1, William Darity, Jr. c,2

a Department of Economics, Washington and Lee University, Lexington, VA 24450, USA

b Freddie Mac, 8200 Jones Branch Drive-Mailstop 289, McLean, VA 22102, USA

c Department of Economics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA

Received 19 September 1998; received in revised form 26 January 2000; accepted 24 May 2000


This paper o?ers a test of the relative wage version of the eciency wage hypothesis ± that

®rms are able to improve worker productivity by paying workers a wage premium. Psychologists

believe work e?ort re¯ects motivation that is governed by a feature of personality referred

to as locus of control. Measures of locus of control are available in the National

Longitudinal Survey of Youth, Using data drawn from the NLSY in 1992 we simultaneously

estimate structural real wage and e?ort equations. We ®nd that receiving an eciency wage

enhances a person’s e?ort and that person’s providing greater e?ort earn higher wages.

Ó 2000 Elsevier Science B.V. All rights reserved.

PsycINFO classi®cation: 3000; 3630

JEL classi®cation: E24; J6

Keywords: Locus of control; Employee motivation; Salaries; Employee bene®ts

Journal of Economic Psychology 21 (2000) 351±385

* Corresponding author. Tel.: +1-540-463-8970; fax: +1-540-463-8639.

E-mail address: (A.H. Goldsmith).

1 Tel.: +1-703-903-3274; fax: +1-703-903-2814.

2 Tel.: +1-919-966-2156; fax: +1-919-966-4986.

0167-4870/00/$ – see front matter Ó 2000 Elsevier Science B.V. All rights reserved.

PII: S 0 1 6 7 – 4 8 7 0 ( 0 0 ) 0 0 0 0 8 – 8

1. Introduction and statement of the problem

This paper o?ers a test of the relative wage version of the eciency wage

hypothesis. This form of the eciency wage hypothesis states that ®rms are

able to improve worker productivity by paying workers a wage premium ± a

wage that is above the wage paid by other ®rms for comparable labor. A link

between wage premiums and productivity might arise for a number of distinct

reasons. A wage premium may enhance productivity by improving

nutrition (Leibenstein, 1957), boosting morale (Solow, 1979), encouraging

greater commitment to ®rm goals (Akerlof, 1982), reducing quits and the

disruption caused by turnover (Stiglitz, 1974), attracting higher quality

workers (Stiglitz, 1996; Weiss, 1980), and inspiring workers to put forth

greater e?ort (Shapiro & Stiglitz, 1984).

Much attention (Krueger & Summers, 1988; Dickens & Katz, 1987) has

focused on whether ®rms pay eciency wages. 3 Another line of inquiry

(Leonard, 1987; Groshen & Krueger, 1990) has explored whether ®rms that

pay wage premiums recoup some of the costs by allocating less resources for

employee supervision. 4 Economists have taken the position that e?ort is not

only imperfectly observed by the employer, but that it also is unobserved for

the investigator or econometrician. Thus, economists have been unable to

examine the impact of wage premiums on e?ort and hence directly test the

eciency wage hypothesis. 5

As a result, Allen (1984) opted to probe indirectly the eciency wage

hypothesis by investigating the impact of wage premiums on an observable,

3 These researchers report that workers with similar skills and job characteristics earn substantially

di?erent wages. The standard competitive labor market model does not provide a straightforward

explanation of the persistence of such di?erentials for comparable labor. They interpret their ®nding as

evidence that ®rms pay eciency wages.

4 Leonard (1987) ®nds no signi®cant evidence of a trade-o? between supervisory intensity and wage

premiums. Groshen and Krueger (1990) report that enhanced supervision leads to lower wages for nurses,

but in three other occupations (e.g. food service employees, radiographers, and physical therapists) pay is

found to be statistically independent of the level of supervision.

5 A rare exception is an unpublished exploratory study of the relation between wage premiums and selfreported

work e?ort conducted by Krueger and Summers (1986) using data from the 1977 Quality of

Employment Survey. They use OLS to estimate an equation where self-reported work e?ort is the

dependent variable; it is a qualitative limited dependent variable ranging from 1±4. The wage premium is

speci®ed to be exogenous and a limited set of control variables is included in their e?ort equation. They

®nd that a greater wage premium has a positive, but statistically insigni®cant, impact on self-reported

work e?ort.

352 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385

absenteeism, that is likely to be related to productivity. 6 An alternative tactic

has been to concentrate on testing the predictions of the labor turnover

(Campbell, 1993; Leonard, 1987; Krueger & Summers, 1988) and shirking

(Cappelli & Chauvin, 1991) versions of the eciency wage theory, since quit

behavior is readily observed and data are available on disciplinary dismissals

± a potential measure of shirking.

Psychologists believe work e?ort re¯ects motivation, which is governed by

a feature of personality, referred to as locus of control. In their view, locus of

control can be detected by employers, can be measured by investigators, and

can be used as a measure of e?ort. Measures of locus of control are construed

by psychologists as an index of e?ort and are available in the National

Longitudinal Survey of Youth (NLSY).

In this paper, we use data drawn from the NLSY to advance two

questions germane to the eciency wage literature that economists have

yet to explore. First, are workers who receive an eciency wage likely to

exhibit greater e?ort? Second, are wages enhanced by improved e?ort? A

real wage equation is estimated to identify the contribution of e?ort to

hourly compensation. We estimate an individual e?ort equation also to

determine if earning an eciency wage, and other factors that a?ect the

perceived cost of job loss, in¯uence e?ort. We introduce a new method of

measuring a person’s eciency wage into the eciency wage literature. In

our empirical work a person earns an eciency wage when they earn

more than they expect to earn given their personal characteristics rather

than earning more than a typical worker in their industry or occupation


This paper is organized as follows: In Section 2 we present a brief review of

the relative wage version of the eciency wage hypothesis. The model guides

our subsequent empirical work. In Section 3 we discuss the literature from

the ®eld of psychology that advances a relationship between personality and

e?ort. Also, based on this knowledge, we describe and evaluate the measurement

of e?ort. Section 4 contains a description of our empirical procedures,

including data, model speci®cation, and estimation technique. An

alternative paradigm for explaining the relation between economic outcomes

and e?ort, based on stress process theory, is discussed in this section. We

6 In a similar line of inquiry, Hamermesh (1977) found that high wages enhance job satisfaction ± which

he believes is measurable ± that, in turn may promote productivity.

A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 353

present estimates of the impact on e?ort of receiving an eciency wage,

unemployment and other factors that in¯uence the perceived cost of job loss

in Section 5. This section also contains our estimates of the determinants of

wages, including the contribution of e?ort. The implications of our ®ndings

and concluding remarks appear in Section 6.

2. Monitoring, e?ort, and wages

The basic tenet of eciency wage theory is that e?ort, e, depends on

compensation. Shapiro and Stiglitz (1984), founders of the shirking variant

of the ef®ciency wage theory, contend that effort must be elicited from

workers through either external monitoring, ME, or internal monitoring, MI.

External monitoring occurs when ®rms utilize supervisors and equipment to

oversee work effort. Shapiro and Stiglitz (1984) and Krueger and Summers

(1988) claim that external monitoring is costly and impractical in some industries

and occupations. Due to technology and the manner in which work

is organized it may be dif®cult to observe an individual employee’s contribution

to output. Under these conditions, how can employers elicit greater

effort from their employees?

The fundamental insight in shirking models is that more e?ort can be

obtained by providing incentives for workers to “internally monitor” (or selfmonitor).

Workers self-monitor when they view their job as relatively attractive.

Therefore, workers receiving a wage, w, above what they could

command if employed elsewhere, w, or those earning a wage premium,

?w ÿ w > 0?, are expected to internally monitor.

The extent to which workers self-monitor is a?ected by factors that in-

¯uence the perceived cost of job loss besides the wage rate. These factors

include items such as the odds of being exposed to job loss, availability and

generosity of unemployment insurance, transferability of skills, household

wealth, earnings of other family members, and the perceived psychological

e?ect of exposure to joblessness. In addition, early childhood socialization

establishes attitudes toward work intensity and self-governance, which in-

¯uence the propensity to self-monitor.

In work environments where it is easy for managers to observe and evaluate

workers, external monitoring is accurate and cost e?ective. When external

monitoring is dicult there is an incentive for management to establish

policies that foster internal monitoring.

354 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385

3. Personality and e?ort

Individuals who exert higher levels of e?ort on the job are expected to

exhibit greater productivity. Psychologists treat e?ort as the response to an

underlying motivation. Thus, theories of motivation can be viewed as theories

of e?ort. Economists (Kim & Polachek, 1994) recognize that motivation

di?ers across individuals and is likely to in¯uence their productivity. How

else can we explain the hardworking individual with modest skills who

consistently outperforms other more gifted persons? The motivated are

generally characterized as contributing an abnormally strong commitment to

the tasks they face.

The founders of motivational theory (Atkinson, 1964; Vroom, 1964) hypothesized

that motivation depends upon motives and expectancies. Motives

are best thought of as an orientation, disposition, or taste to seek or to avoid

various behaviors. Psychologists believe motives are established early in life

and remain stable over the life cycle (Atkinson, 1964, p. 242). Expectancies

entail an individual’s assessment of the likelihood that their actions will result

in attainment of a desired outcome. Bandura (1986), the founder of social

learning theory, refers to a person’s expectancy in a speci®c domain as selfef

®cacy. According to Bandura (1986) motivation to initiate action is governed

by motives, which are time-invariant, and self-ef®cacy that responds to

salient events including labor market outcomes such as unemployment. 7

Economists Summers (1988), Shapiro and Stiglitz (1984), and Yellen (1984)

have argued that compensation and “fear of unemployment” induce motivation

at the workplace.

Currently, among psychologists, expectancy theory is the most widely

accepted and empirically supported theory of motivation (Robbins, 1993;

Muchinsky, 1977). Expectancy theory has its roots in the motivation theory

developed by Atkinson (1964) and Vroom (1964). According to expectancy

theory, the strength of a person’s motivation depends on the extent to which

they believe that “exertion, performance, and reward” are linked tightly. 8

7 Psychologists also have asserted that motivation depends upon satisfaction of needs (Maslow, 1954),

goal-setting (Locke, 1968), and equity (Adams, 1965).

8 This theory posits that a person’s motivation is directly related to their belief that: (1) e?ort will lead to

performance ± like achievement of the attempted task; (2) performance will be rewarded by compensation,

opportunity to use skills, security, and the chance to develop professional relations; and (3) the rewards

contribute to the realization of individual goals ± like self-respect, status, recognition, friendship, and


A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 355

Attribution theorists (Heider, 1958; Rotter, 1966) have proposed that an

aspect of personality ± locus of control ± governs a person’s perception of the

relation between exertion, performance, and reward.

Rotter (1966) classi®ed individuals who believe they are masters of their

own fates, and hence bear personal responsibility for what happens to them,

as “internalizers”. Internalizers see control of their lives as coming from

within themselves. On the other hand, many people believe that they are

pawns of fate, that they are controlled by outside forces over which they have

little, if any, in¯uence. Such people feel that their locus of personal control is

external rather than internal, and they bear little or no responsibility for what

happens to them. Rotter referred to the latter group as “externalizers”.

Expectancy theory predicts that a person with a more internal locus of

control will be more motivated than a comparable individual whose locus of

control is external because internalizers see themselves as “in-control”, i.e.

able to produce desired outcomes (Skinner, Chapman & Baltes, 1988).

Skinner (1995, pp. 69,70) asserts that the primary psychological mechanism

by which perceived control in¯uences outcomes is through its e?ects on action

or motivation. 9 According to Bandura (1989, p. 1176) a person’s beliefs

about their capabilities to exercise control over events ± locus of control ±

“determines their level of motivation, as re¯ected in how much e?ort they

will exert in an endeavor and how long they will persevere in the face of


Dunifon and Duncan (1998, p. 34) claim that

Because of the importance attached to motivation by personality psychologists

motivational measures were included in both the National

Longitudinal Surveys (NLS) and the National Longitudinal Survey of

Youth (NLSY) labor-market panels, and the early waves of the Panel

Study of Income Dynamics (PSID). For the original NLS cohorts all

23 items from Rotter (1966) `locus of control’ scale were included as a

measure of expectancy; in the NLSY, a four-question subset of these

was included. . . The PSID-based expectancy items are essentially equivalent

to this subset of Rotter’s scale. . .

9 Skinner (1996) found that researchers use a large number of terms to describe control. Some constructs

include the term “control” in their name ± locus of control, personal control, sense of control ± while

others do not explicitly use the term ± self-ecacy, mastery, helplessness ± but nevertheless are closely

related. Thus, terms like locus of control and self-ecacy are comparable and used interchangeably.

356 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385

Thus, the designers of the NLS and PSID anticipated that measures of

expectancy would be used as indexes of motivation or e?ort. A number of

investigators, including Goldsmith, Veum and Darity (1999), Duncan and

Dunifon (1998), Dunifon and Duncan (1998) and Hill et al. (1985) have

adopted this means of measuring motivation. 10

Direct evidence that locus of control in¯uences motivation comes from a

number of sources. Studies by Harter (1978) and Kuhl (1981) reveal that

when perceived control is high, a person tends to embrace challenges, construct

more e?ective action plans and exert more sustained e?ort in their

enactment. Heckhausen (1991) and Kuhl (1984) reach a similar conclusion.

They ®nd that people with high control are better able to concentrate completely

on tasks, enhancing access to their working memory and boosting

their persistence in the face of obstacles. Bandura and Cervone (1983) found

individuals with a stronger belief that they are in control exert greater e?ort

to master a challenge and are more persistent in their e?orts. In addition,

when actions do not initially succeed, people with high control are more

likely to increase their e?ort exertion and continue to try to achieve their goal

(Bandura, 1989; Dweck, 1990; Jacobs, Prentice-Dunn & Rogers, 1977; Baum,

Fleming & Reddy, 1986). These ®ndings corroborate the earlier ®ndings of

Seligman (1975) that repeated exposure to uncontrollable events, leading to

feelings of helplessness and an external outlook, reduces motivation to engage

in goal-directed behavior. 11

Bandura (1989) and Dweck (1990) believe that persons with a greater sense

of control are more productive because they exhibit a pattern of more effective

strategy selection, hypothesis testing, problem-solving, and general

analytic thinking. In summarizing her review of the literature on the relationship

between locus of control and action, Skinner (1996, p. 556) stated

“when people perceive that they have a high degree of control, they exert

10 Psychologists Skinner et al. (1988) assert that perceived control depends on three conceptually

independent sets of beliefs; control beliefs, expectancies about the extent to which a person can obtains

desired outcomes, means±ends beliefs, expectations about what factors produce outcomes; and agency

beliefs, opinions about the possession of various means. They provide evidence that effort is most closely

associated with means±ends beliefs. However, they also report a positive and statistically signi®cant

relation between effort and control beliefs. Thus, using control beliefs as a proxy for motivation is viable.

11 Maier and Seligman (1976) argue that once events or socialization lead an individual to hold a

particular locus of control or e?ort level, their view of the link between action and outcome, and hence

motivation, is transferred to all other situations they encounter. Thus, if a person ®nds that attempts to

succeed in school or to succeed socially are unsuccessful, they are not only likely to become apathetic

students and seek the company of others less often, but also would be less motivated workers.

A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 357

e?ort, try hard, initiate action, and persist in the face of failures and setbacks;

they evince interest, optimism, sustained attention, problem solving, and an

action orientation”. In short, persons with a more internal locus of control

are both more motivated and productive.

Psychologists have designed and validated survey instruments capable of

measuring locus of control, and hence, motivation or e?ort. This makes it

possible for economists to explore the reciprocal in¯uences of real wages and

e?ort. The following section discusses the empirical procedures we adopt to

perform such an examination.

4. Empirical procedures

4.1. Data

The data used in this study is from the NLSY. The NLSY is a sample of

12,686 males and females who were between the ages of 14 and 22 in 1979

and who have been interviewed annually since then. The NLSY is a data set

rich in economic and demographic information, including data on wages and

multiple aspects of human capital. It also contains information on motivation.

Motivation or e?ort is expected to depend upon motives and self-ef®cacy.

Motives, a disposition to pursue or evade various behaviors, are established

early in life remain stable and are heavily in¯uenced by socialization. Selfef

®cacy is a variable feature of personality that is likely to respond to salient

experiences, such as occurrences in the labor market. 12 Therefore, holding

motives constant, ¯uctuations in effort can be attributed to variations in selfef


Families and signi®cant others socialize youths and are thereby largely

responsible for the establishment of a person’s motives early in life. The

NLSY contains information describing a person’s adolescent home environment,

which can be used to represent their motives.

The Mastery Scale was developed by Pearlin, Lieberman, Menaghan, and

Mullan (1981) to measure a person’s locus of control or self-ef®cacy. The

NLSY contains each person’s score on the Mastery Scale in 1992. Mastery

12 Gorman (1968), McArthur (1970), and Smith (1970) o?er evidence that contemporary events

in¯uence individuals perceptions of causality and hence control.

358 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385

Scale scores range in value from 0 to 7 (an internal response to each question).

Individuals with a high score ± those with a more internal locus of

control ± are expected to be more motivated than a comparable persons with

lower scores on the Mastery Scale. 13

If Mastery Scale scores are used to measure motivation, because they

gauge self-ecacy, then Pearlin et al.’s (1981) Stress Process Theory, like the

economists eciency wage theory, predicts a direct relation between work

place e?ort and unexpected wages. However, Pearlin’s explanation is

grounded in psychological theory rather than a conjecture about how individuals

respond to economic incentives such as the cost of job loss. Stress

Process Theory links life event with stress and stress with self-ecacy, and

hence, motivation.

Following the seminal work of Cannon (1935) and Selye (1956), Pearlin

et al. (1981) argue that humans are fundamentally intolerant of change. In

their view salient life events either foster or curtail stress. They believe stresses

directly alter aspects of self-concept including “mastery” or self-ecacy.

Thus, earning an eciency wage provides a person with concrete evidence of

their success and proof they are able to alter circumstances of their lives, both

of which reduce life strains and contribute to mastery. Disappointing life

events such as bouts of unemployment would provoke erosion of self-ecacy

and motivation.

Social support and coping behavior are expected to in¯uence the amount

of stress that people experience. Pearlin et al. (1981) believe these elements

are important components of the stress process and in¯uence the motivation

level people exhibit. Pearlin and his colleagues claim, and o?er evidence, that

13 Many economists are sceptical that psychological constructs such as locus of control can be measured

accurately by scales constructed from self-reported evaluations collected in the form of responses to survey

questions. Psychologists assess the usefulness of scales developed to measure a psychological construct

such as locus of control by examining three features of the scale: convergent validity, reliability, and

stability. Convergent validity is concerned with whether an alternative scale seeking to measure the same

construct yields a similar assessment. A scale is reliable when the questions that comprise the scale are all

probing similar or related features of the individual’s make-up. A scale is only considered stable if

administering the same scale a short time in the future generates a similar assessment. Pearlin et al. (1981)

found the Mastery Scale correlated well with other scales used to measure to locus of control. In addition

to meeting the criteria for convergent validity, they discovered the scale was internally consistent, and

stable over time, For a detailed discussion of Mastery Scale Validity, see Seeman (1991, pp. 304±306).

Economists also have an aversion to making inter-personal comparisons using self-reported evaluations

(Easterlin, 1974). For a detailed discussion of both the measurement and comparison issues raised by

economists, and the procedures adopted by psychologists that address these concerns, see Darity and

Goldsmith (1996) and Goldsmith, Veum and Darity (1996a).

A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 359

emotional support characterized by “qualities of trust and intimacy. . .commonly

properties of marital relation”, reduce life strains and thereby contribute

to self-ecacy.

Coping behaviors also are likely to alter the stress levels people experience.

Coping may entail modi®cation of a stressful situation, altering the meaning

associated with undesirable life events, and management of stress symptoms.

People often seek assistance from family members, friends, professional

councillors, and clergy in developing and applying coping skills and strategies.

The NLSY provides a means of measuring motives and self-ef®cacy as well

as social support and coping. Moreover, information on labor market outcomes

and demographic factors are available in the NLSY. Thus, the NLSY

is an ideal data set for an investigation of the relation between effort and

unanticipated wages, which economists refer to as the shirking version of the

ef®ciency wage hypothesis.

4.2. Model speci®cation and hypotheses

Following the convention initiated by Mincer (1962), the productivity, and

hence wage, of a worker is expected to depend on their personal attributes,

such as skills and e?ort, as well as the characteristics of their workplace.

According to the eciency wage hypothesis, a worker’s e?ort depends upon

both external monitoring ± the extent of direct supervision ± and internal

monitoring. Internal monitoring re¯ects early childhood socialization and the

perceived costs of job loss, including the wage a person receives relative to

their expected wage. Therefore, both wages and e?ort should be viewed as

endogenous and determined simultaneously. In order to account for the joint

determination of wages and e?ort, and to allow for the impact of life events

on stress and e?ort, the following two equation structural model is speci®ed:


? ?Si?k ? ?Ai?d ? li; ?4:1?

WAGEi ? a?EFFORTi? ? ?Hi?b ? ?Xi?c ? ei: ?4:2?

Table 1

Variable names, de®nitions, means, and (standard deviations): Wage and e?ort equations

Variable name Variable de®nition All Male Female White Black Hispanic

WAGE Natural log of hourly wage in 1992 2.17












EFFORT Sum of the response to the seven Pearlin

questions used to measure locus of control













EDUCATION Years of education completed at 1992

interview date













EXPERIENCE Weeks of work experience at 1992 interview














TENURE Weeks with current employer at 1992

interview date













JOB TRAINING 1 if received company training from 1992

employer since 1991 interview date,

0 otherwise













AFQT Percentile score on the Armed Forces

Qualifying Test













AGE Age 30.79












UNEMPLOYMENT Local unemployment rate 0.13












UI BENEFITS Average weekly unemployment insurance

bene®t in state of residence in 1992 dollars













SMSA 1 if live in an SMSA, 0 otherwise 0.75














Number of spells of unemployment since

January 1, 1978















Duration of longest unemployment spell

since January 1, 1978













MARRIED 1 if married, 0 otherwise 0.55












SPOUSE EARNINGS Earnings of spouse in 1992 dollars,

0 if single













A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 361

Table 1 (Continued)

Variable name Variable de®nition All Male Female White Black Hispanic

CHILDREN Number of children in household 1.10












PART-TIME 1 if usually work less than 30 hours

per week, 0 otherwise













ASSETS total value of ®nancial assets in 1992 12751












MALE 1 if male, 0 otherwise 0.53








BLACK 1 if black, 0 otherwise 0.27






HISPANIC 1 if Hispanic, 0 otherwise 0.19








1 if occupation of either parents was

professional or manager when respondent

was 14, 0 otherwise













BOTH PARENTS 1 if both parents lived in household when

respondent was 14, 0 otherwise















Average highest grade completed by

respondent’s parents













RELIGION 1 if aliated with any religious group,

0 otherwise













362 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385



Number of employees at establishment 538














1 if company has employees at another

location, 0 otherwise















1 if employer has 1000 or more employees

at other locations













UNION 1 if member of a union, 0 otherwise 0.14












NORTHEAST 1 if lived in Northeast region, 0 otherwise 0.16












NORTH-CENTRAL 1 if lived in North Central region,

0 otherwise













WEST 1 if lived in Western region, 0 otherwise 0.21












IMILLS Selection correction term 0.20












n Number of observations 5579 2933 2646 3013 1509 1057

A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 363

4.2.1. E?ort equation

A person’s level of EFFORTi, the dependent variable in Eq. (4.1), is

measured by their 1992 score on the “Mastery Scale” ± a gauge of self-ef®-

cacy ± since measures of an individual’s motives are included as explanatory

variables in the effort equation. It is interesting to note that Mastery Scale

scores are surprisingly high with 49% of the sample providing self-reports

placing them in the highest motivation category. However, there is substantial

variability in the remaining responses with 44% of all scores ranging

between 4 and 6.

The vector Si contains a cluster of variables describing an individual’s

adolescent home environment of age 14 to account for the in¯uence of socialization

on the formation of motives. Measures of PARENT EDUCATION,

whether a PROFESSIONAL PARENT resides in the home, and the

presence of BOTH PARENTS are included in Si.

Self-ef®cacy, later in life, is likely to be enhanced by an adolescence where

BOTH PARENTS are present, a PROFESSIONAL PARENT resides in the

home, and PARENT EDUCATION is greater. Thus, including Si as an

explanatory variable in the effort equation serves two purposes; it captures

the contribution of motives to subsequent self-ef®cacy, and accounts for the

“trait-like” component of motivation. Thus, holding constant a person’s

motives, ¯uctuations in self-ef®cacy correspond with movements in effort.

The frequency distribution for Mastery Scale scores in 1992 is presented in

Table 2.

Table 2

Frequency distribution: E?ort scalea

Mastery scale

Score Frequency Percent

0 10 0

1 53 1

2 144 2

3 263 4

4 462 7

5 859 13

6 1635 24

7 3321 49

n 6747 100

a E?ort, e, is measured by a person’s score on the Pearlin et al. (1992) Mastery Scale. The distribution

presented is for all persons in the sample in 1992 whether or not they were working ± the sample used to

estimate the reduced form effort and wage equations. The distribution is similar to the distribution for

those who were employed at the time of the 1992 survey.

364 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385

In our view a worker receives an eciency wage when they are earning a

WAGEi greater than the wage they expect to earn, EXPECTED WAGEi. In

prior studies (Leonard, 1987; Krueger & Summers, 1988) the wage premium

expected to induce greater e?ort is measured by the di?erence between what

an individual earns and the average wage in their occupation. However, an

individual is likely to believe they are earning a wage premium only when

they earn more than what they expect to earn based upon their personal

characteristics ± which may di?er from those of the average person in their

occupation. A person earning an eciency wage would ®nd job loss to be

especially costly. Thus, individuals who receive an EFFICIENCY WAGE,

?WAGEi ÿ EXPECTED WAGEi? > 0, are expected to monitor internally to

a greater extent and to o?er their employer greater e?ort.

The vector Ci is composed of the remaining factors that are likely to

determine the perceived cost of job loss. Workers may fear long, and hence

costly, bouts of unemployment. Thus, a rise in the local UNEMPLOYMENT

rate, which portends longer spells for those who become jobless, will

prompt greater e?ort to reduce the likelihood of discharge for inadequate

performance. In contrast, the bigger the local labor market the easier it is to

®nd a desirable job. Thus, we might expect that individuals who live in a

larger SMSA will be inclined to provide less e?ort on the job. Residents of

states with more generous unemployment insurance, greater UI BENEFITS,

face a smaller cost of job loss and are presumed to extend less e?ort

at work.

Unemployment generates ®nancial and psychological hardships (Goldsmith,

Veum & Darity, 1996b). These consequences of unemployment are

likely to be more vivid or salient for persons who in the past have been exposed

to UNEMPLOYMENT BOUTS more often and have experienced

greater UNEMPLOYMENT DURATION. Therefore, greater personal exposure

to joblessness may enlarge the perceived costs of unemployment

leading to more e?ort in an attempt to prevent experiencing unemployment

again. Alternatively, individual’s with unemployment in their past may become

helpless and fatalistic, believing that the likelihood of experiencing

unemployment in the future is independent of their current level of e?ort on

the job. If this were the case, workers with more and longer bouts of unemployment

in their past may choose to give less e?ort than comparable

employees with better labor market histories. Hence, the impact of prior

unemployment on current e?ort levels is ambiguous.

People who have accumulated more transferable human capital are

likely to be less fearful of unemployment and, therefore, more prone to

A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 365

o?er their employers less e?ort. It is possible also that workers with more

human capital secure jobs they enjoy and are attached to leading them to

o?er their employers greater e?ort. Measures capturing these di?erent aspects

of general human capital are contained in the vector Ci. Broad-based

formal skills are captured by EDUCATION. An individual’s verbal and

mathematical skills developed while attending school and at home are

measured by scores on the Armed Forces Qualifying Exam, AFQT (see

Fischer et al., 1996, pp. 55±69). General workplace skills are represented


Job loss is costly for workers who possess non-transferable or ®rm speci®c

skills, leading those with non-transferable skills to give greater e?ort on the

job to avoid losing the skills they have required. Following Becker (1962),

TENURE and JOB TRAINING, which are included in Ci are often described

as forms of ®rm speci®c human capital. However, TENURE and

formal training received on the job may provide workers with both general

and ®rm-speci®c skills (Neal, 1995). Thus, the impact of longer TENURE

and JOB TRAINING on e?ort is ambiguous, depending on the composition

of the skills acquired.

More mature young workers (those of greater AGE), with a given set of

skills and experiences, are likely to have learned the employer’s minimally

acceptable standard of e?ort. Younger workers who have yet to discover this

level may provide more e?ort, to be viewed as o?ering an adequate level of

job performance. 14

Membership in a UNION reduces the probable costs of job loss by providing

®nancial bene®ts and job location assistance. Part-time jobs are usually

available but are unlikely to be viewed as career positions. Thus, losing a

part-time position is perceived to be less damaging than losing a full-time

appointment, leading PART-TIME employees to provide less e?ort. On the

other hand, PART-TIME employees may provide extraordinary e?ort to

enhance their likelihood of being o?ered a full-time position when one becomes


Job loss is probably viewed as particularly burdensome to people with

more CHILDREN. The responsibilities associated with child rearing are

expected to inspire greater e?ort. As SPOUSE EARNINGS rise the per-

14 Because we are controlling for tenure and general work experience, age is a biological or a real time

variable here. However, the age spread is so small across our sample that it cannot really capture

important life-cycle tissues. It is best interpreted as a learning variable.

366 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385

ceived costs of job loss fall and, most likely, e?ort. Similarly, individuals with

greater ®nancial ASSETS will be less fearful of job loss and, ceteris paribus,

will offer less effort on the job.

Women and minorities may believe that discrimination makes it dicult

to secure comparable employment if they are discharged. If so, they face a

higher perceived cost of job loss. Thus, BLACK and HISPANIC workers

are expected to give greater e?ort than otherwise equivalent white employees

do, while MALE workers are expected to exert less e?ort relative to


Persons who are MARRIED are expected to bene®t from superior social

support, relative to comparable individuals who are not married, leading to

a greater sense of self-ecacy and motivation. Individuals who grew up in

households that were aliated with a RELIGION are likely to have

developed coping skills and strategies that contribute to self-ecacy or


Firms can use external monitoring to extract greater e?ort from their work

force. However, as the number of employees at a work site expands, it becomes

more dicult to detect a worker’s intensity on the job. Therefore,

greater ESTABLISHMENT SIZE may diminish worker e?ort. On the other

hand, larger ®rms provide more opportunities for advancement, which may

motivate workers. Thus, it is unclear how ESTABLISHMENT SIZE will

in¯uence worker e?ort. Firms with MULTIPLE LOCATIONS or work sites,

particularly if they are LARGE MULTIPLE LOCATIONS, o?er more

opportunities for professional advancement. Workers identi®ed as giving

greater e?ort are more likely to be granted transfer promotions. Thus, individuals

employed by such ®rms are expected to engage in more internal

monitoring and to extend greater e?ort on the job. The vector Ai contains

three variables representing ®rm characteristics that may in¯uence the extent

of external monitoring workers face, as well as likely employee commitment

to internal monitoring.

Jobs that are challenging and provide workers a high degree of autonomy

are expected to induce greater e?ort from workers controlling for the level of

external monitoring. MANAGEMENT, PROFESSIONAL and CRAFT

positions may o?er these desirable work characteristics relative to LABORER

jobs. Thus, the e?ort equation includes dummy variables that

identify occupation of employment. To account for the possibility that

worker e?ort varies systematically across industries, ceteris paribus, dummy

variables for industry of employment also are included in the effort equation

(Eq. (4.1)).

A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 367

4.2.2. Wage equation

Eq. (4.2) stipulates that individuals who expend greater e?ort, ei, and

possess more human capital, Hi, command a higher real wage. The vector Xi

contains a standard set of demographic (e.g. race, gender, marital status,

dependents) and work place (e.g. occupation, industry, local unemployment

rate, ®rm size, union) wage equation regressors.

The wage a person receives also may be a?ected by the region of the US in

which they are employed. Controlling for personal characteristics and labor

market factors Kiefer and Smith (1977) and Sahling and Smith (1983) o?er

evidence that signi®cant regional wage di?erentials exist for otherwise comparable

workers. These pay di?erences may re¯ect cultural and institutional

variation in setting pay scales in internal labor markets, and incomplete responses

to regional labor market shocks. To account for the in¯uence of

region of employment on wages, Xi contains dummy variables to identify

employment in the WEST, NORTHEASTS, and NORTHCENTRAL regions

of the US.

A person’s WAGEi relative to their expected wage, EXPECTED WAGEi,

appears in the e?ort equation (4.1), and EFFORTi is included in the wage

equation (4.2). This accounts for the joint determination of both WAGEi and

EFFORTi. EFFORTi is independent of the region of the country where an

individual is employed (WEST, NORTHEAST, NORTHCENTRAL) which

is expected to affect a person’s WAGEi. As a result, these regional dummy

variables are used to identify the effort equation, Eq. (4.1). Variables re-

¯ecting early childhood socialization (BOTH PARENTS, PARENT EDUCATION,

PROFESSIONAL PARENT), and household ®nancial factors

(SPOUSE EARNINGS, ASSETS) are expected to exert a direct in¯uence on

EFFORTi while only indirectly effecting WAGEi, through their impact on

EFFORTi. Because these variables are included in the effort equation but are

excluded from the wage Eq. (4.2), they identify the wage equation. 15

15 Frantz (1982) estimates a similar model to explore the relation between wages and changes in

attitudes. Using data from the National Longitudinal Survey of Young Men he jointly estimates a wage

equation and a change in attitude equation, where attitudes are measured by locus of control scores. In

contrast, we jointly estimate wages and locus of control. In addition, the equation we specify to explain

locus of control (e?ort) di?ers from the equation Frantz uses to explain locus of control (self-con®dence),

since we are estimating a model to test the eciency wage hypothesis. Thus, in our model e?ort depends on

factors in¯uencing the cost of job loss such as; earning an eciency wage (ie. a wage greater than

expected), educational accumulation, personal unemployment history, and the generosity of unemployment

bene®ts, which are not included in the attitude change equation estimated by Frantz.

368 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385

4.3. Estimation technique

Two-stage least squares (2SLS) is used to estimate Eqs. (4.1) and (4.2). In

Stage I each endogenous variable is regressed on all of the exogenous variables

in the system by OLS. Using the coecient estimates from these reduced

form equations, we create estimated values of the endogenous variables or

instruments. 16 The estimated values of WAGEi and EFFORTi, are denoted


In Stage II, PREDICTED EFFORTi, which is uncorrelated with ei, the

wage equation error term, replaces EFFORTi ± which is correlated with ei ±

in Eq. (4.2). A person’s PREDICTED WAGEi, controlling for whether the

person is participating currently in the labor force, is likely to be equivalent

to their EXPECTED WAGEi. Therefore, a person’s EFFICIENCY WAGEi

± the di?erence between WAGEi and PREDICTED WAGEi is ei, the error

term in Eq. (4.2). In Stage II, ei ± a person’s unexpected wages ± is used as a

measure of this individual’s eciency wage in Eq. (4.1). A standard assumption

when estimating equations simultaneously is that cross equation

error terms are uncorrelated. Thus, we assume that ei is uncorrelated with li,

the e?ort equation error term. The structural equations are then estimated by

ordered probit and OLS, respectively. 17

Wages are observed only for those individuals working for pay. Heckman

(1979a,b) has suggested that unobservable features of an individual both

govern a person’s decision on whether or not to participate in the labor force

and their productivity, if they opt to work. If these factors are omitted from

the estimated equations, then the coecients will su?er from selectivity bias.

Following Heckman, a selection±correction variable (IMILLS) is included in

Eq. (4.2), the wage equation. 18 Since the unobservables that inspire a person

16 It might be argued that using a nonlinear estimation technique is more appropriate given that

EFFORTi as measured by a person’s score on the Mastery Scale is a non-continuous dependent variable.

However, predicted means and actual means can vary substantially using nonlinear methods. Fortunately,

the coecients from a OLS estimation, which are used to create the predicted values, are consistent; only

the standard errors are inconsistent. See Heckman (1979a,b) for a detailed discussion of these points.

17 Ordered probit is an appropriate procedure when the dependent variable is categorical and

sequential, such as our Mastery Scale measure of locus of control, and when errors are assumed normally

distributed (Maddala, 1983).

18 As suggested by Heckman (1979a,b) a preliminary regression is run to explain the probability of

working for pay. This equation is estimated as a Probit model and the resulting coecients are used to

construct (IMILLS),, the inverse Mills ratio. A table with the results of the probability of working for pay

equation is available from the authors upon request.

A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 369

to participate in the labor force are factors that are likely to also improve

e?ort, (IMILLS) is included in Eq. (4.1), the e?ort equation.

5. Results

The system of equations describing the joint determination of EFFORT

and WAGES, Eqs. (4.1) and (4.2), was estimated separately by gender, race,

and ethnicity using data drawn from the NLSY in 1992. For each of these

data sets, the results for the structural e?ort equation appear in Table 3.

Table 4 presents our estimates of the structural wage equation. 19

5.1. E?ort

The results in Table 3 indicate that receiving a greater EFFICIENCY

WAGE signi®cantly enhances a worker’s e?ort for each of the data sets. Thus,

we ®nd evidence consistent with the eciency wage hypothesis. 20 This ®nding

is also consistent with stress process theory ± unexpectedly high earning reduce

life stresses and enhances self-ecacy and hence e?ort. To explore

whether the impact of earning an eciency wage on e?ort varies by industry

Table 3

Structural ordered probit e?ort estimatesa

Variable name

(expected sign)

All Male Female White Black Hispanic


WAGE (+)

0.15 (4.26) 0.17 (3.62) 0.12 (2.16) 0.14 (2.93) 0.15 (2.01) 0.17 (2.20)



0.09 (1.92) 0.12 (1.76) 0.59 (0.85) 0.40eÿ02 (0.06) 0.35 (2.24) 0.12 (1.44)

UI BENEFITS (ÿ) 0.90eÿ04 (0.15) ÿ0.19eÿ03 (0.23) 0.44eÿ03 (0.52) ÿ0.79eÿ03 (0.96) 0.23eÿ02 (1.96) ÿ0.16eÿ03 (0.10)

SMSA (ÿ) 0.25eÿ01 (0.67) 0.16eÿ01 (0.31) 0.35eÿ01 (0.63) 0.68eÿ02 (0.13) 0.22eÿ01 (0.29) ÿ0.95eÿ01 (0.97)



ÿ0.14eÿ01 (2.50) ÿ0.13eÿ01 (1.60) ÿ0.15eÿ01 (1.67) ÿ0.18eÿ01 (2.16) ÿ0.20eÿ01 (1.73) ÿ0.32eÿ02 (0.25)




(2.63) ÿ0.11eÿ02 (1.13) ÿ0.28eÿ02 (2.47) ÿ0.18eÿ02 (1.53) ÿ0.25eÿ02 (2.23) ÿ0.47eÿ03 (0.25)

EDUCATION (?) 0.29eÿ01 (2.81) 0.25eÿ01 (1.74) 0.30eÿ01 (2.00) 0.16eÿ01 (1.08) 0.46eÿ01 (2.20) 0.44eÿ01 (2.04)

EXPERIENCE (?) 0.25eÿ03 (0.81) 0.37eÿ03 (0.80) 0.11eÿ03 (0.23) 0.10eÿ03 (0.23) 0.12eÿ03 (0.19) 0.83eÿ03 (1.24)

AFQT (?) 0.58eÿ02 (6.97) 0.66eÿ02 (5.70) 0.49eÿ02 (3.96) 0.41eÿ02 (3.68) 0.11eÿ01 (5.76) 0.74eÿ02 (3.74)

TENURE (?) ÿ0.19eÿ03 (1.85) ÿ0.98eÿ04 (0.70) ÿ0.29eÿ03 (1.94) ÿ0.29eÿ03 (2.11) ÿ0.71eÿ04 (0.34) ÿ0.17eÿ03 (0.68)

JOB TRAINING (?) 0.45eÿ01 (0.80) 0.26eÿ01 (0.32) 0.47eÿ01 (0.59) 0.23eÿ02 (0.03) 0.16eÿ01 (0.14) 0.17 (1.26)

AGE (ÿ) ÿ0.45eÿ01

(4.96) ÿ0.56eÿ01

(4.33) ÿ0.34eÿ01

(2.56) ÿ0.45eÿ01

(3.59) ÿ0.45eÿ01

(2.46) ÿ0.49eÿ01


UNION (ÿ) 0.65eÿ01 (1.37) 0.81eÿ01 (1.27) 0.29eÿ01 (0.40) 0.62eÿ01 (0.88) ÿ0.12eÿ01 (0.14) 0.13 (1.23)

PART-TIME (?) ÿ0.98eÿ01 (1.86) ÿ0.21 (2.19) ÿ0.32eÿ01 (0.49) ÿ0.11 (1.48) ÿ0.11 (0.10) ÿ0.14 (1.10)

CHILDREN (+) ÿ0.12eÿ01 (0.42) ÿ0.25eÿ01 (1.06) ÿ0.13eÿ01 (0.58) ÿ0.10eÿ01 (0.43) ÿ0.67eÿ02 (0.24) ÿ0.26eÿ01 (0.77)



0.35eÿ05 (2.81) 0.63eÿ05 (2.56) 0.30eÿ05 (1.89) 0.37eÿ05 (2.30) 0.17eÿ05 (0.66) 0.33eÿ05 (0.99)

ASSETS (ÿ) 0.18eÿ05 (2.48) 0.15eÿ05 (1.73) 0.25eÿ05 (1.94) 0.13eÿ05 (1.73) 0.59eÿ05 (2.10) 0.66eÿ05 (2.43)

MALE (ÿ) 0.13 (3.31) 0.15 (2.90) 0.16 (2.18) 0.50eÿ01 (0.53)

BLACK (+) 0.89eÿ01 (1.96) 0.82eÿ01 (1.31) 0.10 (1.55)

HISPANIC (+) 0.72eÿ01 (1.50) 0.30eÿ03 (0.46) 0.99eÿ01 (1.38)

MARRIED (+) 0.81eÿ01 (1.85) 0.11 (1.73) 0.20eÿ01 (0.31) 0.13 (2.11) 0.23eÿ01 (0.28) 0.47eÿ01 (0.46)

RELIGION (+) ÿ0.62eÿ01 (0.80) ÿ0.11 (1.10) 0.45eÿ02 (0.04) 0.37eÿ02 (0.04) ÿ0.12eÿ01 (0.89) ÿ0.33eÿ01 (1.14)


SIZE (ÿ)

0.65eÿ05 (0.89) 0.77eÿ05 (0.71) 0.67eÿ05 (0.67) 0.70eÿ05 (0.71) 0.79eÿ05 (0.58) 0.12eÿ04 (0.62)

A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 371

Table 3 (Continued)

Variable name

(expected sign)

All Male Female White Black Hispanic



0.19eÿ03 (1.00) 0.72eÿ01 (1.31) ÿ0.91eÿ01 (1.54) ÿ0.69eÿ01 (1.27) 0.88eÿ01 (0.11) 0.17eÿ01 (1.77)



ÿ0.28eÿ01 (0.68) ÿ0.80eÿ01 (1.38) 0.40eÿ01 (0.68) ÿ0.35eÿ01 (0.61) 0.19eÿ01 (0.24) ÿ0.89eÿ01 (0.94)



ÿ0.67eÿ01 (1.61) ÿ0.36eÿ01 (0.61) ÿ0.11 (1.86) ÿ0.54eÿ01 (1.04) ÿ0.95eÿ01 (0.97) ÿ0.45eÿ01 (0.39)



0.18eÿ02 (0.48) ÿ0.63eÿ02 (0.12) 0.47eÿ01 (0.85) ÿ0.25eÿ01 (0.39) 0.49eÿ01 (0.83) 0.39eÿ01 (0.45)



0.11eÿ01 (1.68) 0.48eÿ02 (0.53) 0.16eÿ01 (1.71) 0.17eÿ01 (1.56) 0.46eÿ02 (0.33) 0.42eÿ02 (0.38)

IMILLS (?) 0.90eÿ01 (0.50) 0.21 (0.62) ÿ0.20eÿ01 (0.08) 0.28eÿ01 (0.10) 0.38eÿ01 (0.11) 0.39 (1.05)

n 5579 2933 2646 3013 1509 1057

Log Likelihood ÿ7008 ÿ3578 ÿ3406 ÿ3570 ÿ2029 ÿ1354

Chi square [D.F.] 536 [45] 356 [44] 218 [44] 243 [43] 205 [43] 136 [43]

a All equations include INDUSTRY and OCCUPATION dummy variables (t-statistics in parentheses).

* Statistically signi®cantly di?erent from zero at the 0.1 con®dence level.

** Statistically signi®cantly di?erent from zero at the 0.05 con®dence level.

*** Statistically signi®cantly di?erent from zero at the 0.01 con®dence level.

372 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385

Table 4

Structural OLS log wage estimatesa

Variable name

(expected sign)

All Male Female White Black Hispanic



0.58 (6.86) 0.48 (4.04) 0.35 (4.18) 0.78 (6.59) 0.71 (6.22) 0.13 (4.43)

EDUCATION (+) 0.21eÿ01 (4.50) 0.17eÿ01 (2.67) 0.34eÿ01 (5.56) 0.25eÿ01 (4.22) ÿ0:57 eÿ02 (0.62) 15eÿ01 (1.44)

EXPERIENCE (+) 0.62eÿ03 (5.48) 0.51eÿ03 (2.72) 0.78eÿ03 (5.11) 0.70eÿ03 (4.54) 0.49eÿ03 (2.37) 0.33eÿ03 (1.11)

TENURE (+) 0.44eÿ03 (10.13) 0.36eÿ03 (6.29) 0.44eÿ03 (7.25) 0.54eÿ03 (8.47) 0.52eÿ03 (7.01) 0.19eÿ03 (1.96)

JOB TRAINING (+) 0.14eÿ01 (0.63) 0.63eÿ01 (2.05) ÿ0.88eÿ02 (0.30) 0.51eÿ01 (1.74) ÿ0.60eÿ01 (1.51) 0.22eÿ01 (0.39)

AFQT (+) ÿ0.84eÿ03 (1.44) ÿ0.37eÿ03 (0.44) 0.75eÿ03 (1.18) ÿ0.81eÿ03 (1.25) ÿ0.30eÿ02 (2.41) ÿ0.32eÿ02 (2.52)

AGE (+) 0.13eÿ01 (2.53) 0.16eÿ01 (2.01) ÿ0.13eÿ02 (0.22) 0.18eÿ01 (2.45) 0.24eÿ01 (3.15) 0.18eÿ01 (1.69)

UNION (+) 0.12 (6.35) 0.15 (5.65) 0.10 (3.70) 0.11 (4.08) 0.14 (4.70) 0.13 (2.99)


SIZE (+)

0.79eÿ05 (2.84) 0.90eÿ05 (2.18) 0.91eÿ05 (2.48) 0.19eÿ05 (0.49) 0.72eÿ05 (1.61) 0.18eÿ04 (2.25)



0.31eÿ01 (1.98) ÿ0.93eÿ02 (0.38) 0.71eÿ01 (3.00) 0.95eÿ01 (4.25) 0.31eÿ02 (0.11) ÿ0.10 (2.37)



0.54eÿ01 (3.34) 0.75eÿ01 (3.06) 0.24eÿ01 (1.10) 0.75eÿ01 (3.30) 0.34eÿ01 (1.19) 0.50eÿ01 (1.31)

PART-TIME (ÿ) 0.16eÿ01 (0.72) 0.15 (2.79) ÿ0.44eÿ01 (1.82) 0.44eÿ01 (1.43) ÿ0.17eÿ01 (0.46) 0.17eÿ01 (0.29)

SMSA (+) 0.88eÿ01 (5.96) 0.89eÿ01 (4.32) 0.95 (4.54) 0.14 (6.81) 0.22eÿ01 (0.79) 0.85eÿ01 (2.04)



ÿ0.49eÿ01 (2.44) ÿ0.30eÿ01 (1.02) ÿ0.50eÿ01 (1.88) 0.83eÿ01 (3.07) ÿ0.21 (3.45) ÿ0.16 (4.20)



0.18eÿ02 (0.68) ÿ0.32eÿ02 (0.90) ÿ0.75eÿ03 (0.20) 0.11eÿ01 (2.88) 0.67eÿ02 (1.33) 0.13eÿ01 (2.42)



0.14eÿ02 (3.88) 0.49eÿ03 (1.14) 0.98eÿ03 (1.88) 0.15eÿ02 (2.82) 0.22eÿ02 (3.94) 0.43eÿ03 (0.55)

MALE (+) 0.10 (6.60) 0.91eÿ01 (3.96) 0.19eÿ01 (0.64) 0.14 (3.99)

BLACK (ÿ) ÿ0.85eÿ01


0.10eÿ01 (3.74) ÿ0.28eÿ01 (1.01)

HISPANIC (ÿ) 0.46eÿ03 (0.03) ÿ0.46eÿ02 (0.18) 0.25eÿ01 (0.94)

MARRIED (ÿ) ÿ0.36eÿ01 (1.82) ÿ0.21eÿ01 (0.63) ÿ0.17eÿ01 (0.82) ÿ0.10 (3.16) ÿ0.16eÿ01 (0.61) ÿ0.29 (0.75)

CHILDREN (ÿ) 0.11eÿ02 (0.17) 0.21eÿ01 (2.14) ÿ0.22eÿ02


0.26eÿ02 (0.29) 0.23eÿ02 (0.23) 0.20eÿ01 (1.33)

NORTHEAST (?) 0.14 (7.19) 0.86 (2.82) 0.19 (7.17) 0.12 (4.63) 0.88eÿ01 (2.37) 0.20 (4.12)

A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385 373

Table 4 (Continued)

Variable name

(expected sign)

All Male Female White Black Hispanic




(3.01) ÿ0.28eÿ01 (1.01) 0.48eÿ01 (1.84) ÿ0.83eÿ01

(3.47) ÿ0.69eÿ01 (1.95) ÿ1.5 (1.97)

WEST (?) 0.45eÿ01 (2.19) 0.55 (1.97) 0.79eÿ01 (2.81) ÿ0.45eÿ01 (1.45) 0.15 (5.62) 0.65eÿ01 (1.79)

IMILLS (?) 0.16 (2.42) 0.16 (1.19) 0.24 (3.04) 0.13 (1.40) 0.14 (1.19) 0.55eÿ02 (0.04)

CONSTANT (?) ÿ2.65 (4.82) ÿ1.92 (2.39) ÿ1.16 (2.12) ÿ4.12 (5.16) ÿ3.43 (4.57) ÿ2.38 (2.85)

n 5579 2933 2646 3013 1509 1057

F 98.75 [41, 5537] 45.38 [40, 2892] 52.17 [40, 2605] 52.63 [39, 2973] 33.80 [39, 1469] 16.37 [39, 1017]

R2 0.42 0.39 0.44 0.41 0.47 0.39

a All equations include one-digit industry and one-digit occupation dummy variables (t-statistics in parentheses).

* Statistically signi®cantly di?erent from zero at the 0.1 con®dence level.

** Statistically signi®cantly di?erent from zero at the 0.05 con®dence level.

*** Statistically signi®cantly di?erent from zero at the 0.01 con®dence level.

374 A.H. Goldsmith et al. / Journal of Economic Psychology 21 (2000) 351±385

Table 5

Estimated e?ect of eciency wage on e?ort, and e?ort on the wage: by industrya

(1) (2) (3) (4) (5)

INDUSTRY (Sample size) o?EFFORT?




Wage equation industry

dummy variables: Krueger


Wage equation industry

dummy variables: Goldsmith


Agriculture & Mining (185) ÿ0.75eÿ01 (0.42) 0.64 (0.82) 0.22 (2.96) ÿ0.60eÿ01 (1.58)

CONSTRUCTION (384) 0.14eÿ01 (0.10) 1.62 (3.66) 0.11 (3.18) 0.13 (4.65)

MANUFACTURING (1057) 0.43 (2.16) 0.67 (4.18) 0.91eÿ01 (2.84) Control

TRANSPORTATION (373) 0.35 (2.17) 0.84 (2.22) 0.15 (4.26) 0.83eÿ01 (3.05)

WHOLESALE & RETAIL TRADE (952) 0.19 (2.10) 0.32 (1.44) ÿ0.11 (3.36) ÿ0.22 (10.22)

FINANCE (346) 0.21 (1.21) 0.66 (2.03) 0.55eÿ01 (1.62) 0.95eÿ02 (0.33)

BUSINESS & REPAIR SERVICES (440) 0.18 (1.39) 0.96 (3.02) ÿ78eÿ01 (2.43) ÿ0.10eÿ01 (0.37)

PERSONAL SERVICES & ENTERTAINMENT (298) ÿ0.66eÿ01 (0.54) 0.82 (1.80) ÿ0.22 (6.83)

PROFESSIONAL SERVICES (1179) 0.80eÿ01 (1.02) 0.50 (2.76) ÿ0.82eÿ01 (3.70)

PUBLIC ADMINISTRATION (365) 0.36 (1.91) 0.46 (1.55) ÿ0.13eÿ01 (0.46)

a The coecients reported in columns 2 and 3 are extracted from estimates of Eqs. (4.1) and (4.2) using data on a particular industry ± industry is

identi®ed by row. The remaining coecient estimates are suppressed. Columns 4 and 5 report coecients estimated on industry dummy var


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