Working hard for the money? Eciency wages and worker
eort
Arthur H. Goldsmitha,*, Jonathan R. Veumb,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
Abstract
This paper oers a test of the relative wage version of the eciency wage hypothesis ± that ®rms are able to improve worker productivity by paying workers a wage premium. Psychol-ogists believe work eort re¯ects motivation that is governed by a feature of personality re-ferred 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 eort equations. We ®nd that receiving an eciency wage enhances a person's eort and that person's providing greater eort 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
www.elsevier.com/locate/joep
*Corresponding author. Tel.: +1-540-463-8970; fax: +1-540-463-8639. E-mail address:[email protected] (A.H. Goldsmith).
1Tel.: +1-703-903-3274; fax: +1-703-903-2814. 2Tel.: +1-919-966-2156; fax: +1-919-966-4986.
1. Introduction and statement of the problem
This paper oers a test of the relative wage version of the eciency wage hypothesis. This form of the eciency 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 dis-tinct 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 eort (Shapiro & Stiglitz, 1984).
Much attention (Krueger & Summers, 1988; Dickens & Katz, 1987) has focused on whether ®rms pay eciency 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. 4Economists have taken the position that eort 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 eort and hence directly test the eciency wage hypothesis.5
As a result, Allen (1984) opted to probe indirectly the eciency wage hypothesis by investigating the impact of wage premiums on an observable,
3These researchers report that workers with similar skills and job characteristics earn substantially
dierent wages. The standard competitive labor market model does not provide a straightforward explanation of the persistence of such dierentials for comparable labor. They interpret their ®nding as evidence that ®rms pay eciency 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 self-reported work eort 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 eort 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 eort equation. They ®nd that a greater wage premium has a positive, but statistically insigni®cant, impact on self-reported work eort.
absenteeism, that is likely to be related to productivity.6An 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 eciency wage theory, since quit behavior is readily observed and data are available on disciplinary dismissals ± a potential measure of shirking.
Psychologists believe work eort 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 eort. Measures of locus of control are construed by psychologists as an index of eort 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 eciency wage literature that economists have yet to explore. First, are workers who receive an eciency wage likely to exhibit greater eort? Second, are wages enhanced by improved eort? A real wage equation is estimated to identify the contribution of eort to hourly compensation. We estimate an individual eort equation also to determine if earning an eciency wage, and other factors that aect the perceived cost of job loss, in¯uence eort. We introduce a new method of measuring a person's eciency wage into the eciency wage literature. In our empirical work a person earns an eciency 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 does.
This paper is organized as follows: In Section 2 we present a brief review of the relative wage version of the eciency 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 eort. Also, based on this knowledge, we describe and evaluate the mea-surement of eort. Section 4 contains a description of our empirical proce-dures, including data, model speci®cation, and estimation technique. An alternative paradigm for explaining the relation between economic outcomes and eort, based on stress process theory, is discussed in this section. We
6In 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.
present estimates of the impact on eort of receiving an eciency 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 eort. The implications of our ®ndings and concluding remarks appear in Section 6.
2. Monitoring, eort, and wages
The basic tenet of eciency wage theory is that eort, 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 in-dustries and occupations. Due to technology and the manner in which work is organized it may be dif®cult to observe an individual employee's contri-bution to output. Under these conditions, how can employers elicit greater effort from their employees?
The fundamental insight in shirking models is that more eort can be obtained by providing incentives for workers to ``internally monitor'' (or self-monitor). Workers self-monitor when they view their job as relatively at-tractive. Therefore, workers receiving a wage, w, above what they could command if employed elsewhere, w, or those earning a wage premium, wÿw >0, are expected to internally monitor.
The extent to which workers self-monitor is aected 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 eect 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 eval-uate workers, external monitoring is accurate and cost eective. When ex-ternal monitoring is dicult there is an incentive for management to establish policies that foster internal monitoring.
3. Personality and eort
Individuals who exert higher levels of eort on the job are expected to exhibit greater productivity. Psychologists treat eort as the response to an underlying motivation. Thus, theories of motivation can be viewed as theo-ries of eort. Economists (Kim & Polachek, 1994) recognize that motivation diers 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) hy-pothesized that motivation depends uponmotives andexpectancies. 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 self-ef®cacy. According to Bandura (1986) motivation to initiate action is gov-erned bymotives, which are time-invariant, andself-ef®cacythat 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 moti-vation 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).
8This theory posits that a person's motivation is directly related to their belief that: (1) eort 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 security.
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 eects on ac-tion or motivaac-tion.9According 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 eort they will exert in an endeavor and how long they will persevere in the face of obstacles''.
Dunifon and Duncan (1998, p. 34) claim that
Because of the importance attached to motivation by personality psy-chologists 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 equiv-alent to this subset of Rotter's scale. . .
9Skinner (1996) found that researchers use a large number of terms to describe control. Some constructs
Thus, the designers of the NLS and PSID anticipated that measures of expectancy would be used as indexes of motivation or eort. 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, con-struct more eective action plans and exert more sustained eort in their enactment. Heckhausen (1991) and Kuhl (1984) reach a similar conclusion. They ®nd that people with high control are better able to concentrate com-pletely 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 eort to master a challenge and are more persistent in their eorts. In addition, when actions do not initially succeed, people with high control are more likely to increase their eort 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 en-gage 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 ef-fective strategy selection, hypothesis testing, problem-solving, and general analytic thinking. In summarizing her review of the literature on the rela-tionship 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; andagency 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 andcontrol beliefs. Thus, usingcontrol beliefsas a proxy for motivation is viable.
11Maier and Seligman (1976) argue that once events or socialization lead an individual to hold a
particular locus of control or eort 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.
eort, 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 eort. This makes it possible for economists to explore the reciprocal in¯uences of real wages and eort. 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 motiva-tion.
Motivation or eort is expected to depend upon motivesand 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. Self-ef®cacyis a variable feature of personality that is likely to respond to salient experiences, such as occurrences in the labor market. 12 Therefore, holding motivesconstant, ¯uctuations in effort can be attributed to variations in self-ef®cacy.
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 envi-ronment, which can be used to represent theirmotives.
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
12Gorman (1968), McArthur (1970), and Smith (1970) oer evidence that contemporary events
in¯uence individuals perceptions of causality and hence control.
Scale scores range in value from 0 to 7 (an internal response to each ques-tion). 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-ecacy, then Pearlin et al.'s (1981) Stress Process Theory, like the economists eciency wage theory, predicts a direct relation between work place eort and unexpected wages. However, Pearlin's explanation is grounded in psychological theory rather than a conjecture about how indi-viduals respond to economic incentives such as the cost of job loss. Stress Process Theory links life event with stress and stress with self-ecacy, 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-ecacy. Thus, earning an eciency 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-ecacy 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 oer evidence, that
13Many 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).
emotional support characterized by ``qualities of trust and intimacy. . . com-monly properties of marital relation'', reduce life strains and thereby con-tribute to self-ecacy.
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 strate-gies.
The NLSY provides a means of measuringmotivesandself-ef®cacyas well as social support and coping. Moreover, information on labor market out-comes 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 eort, as well as the characteristics of their workplace. According to the eciency wage hypothesis, a worker's eort 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 eort should be viewed as endogenous and determined simultaneously. In order to account for the joint determination of wages and eort, and to allow for the impact of life events on stress and eort, the following two equation structural model is speci®ed:
EFFORTi/ WAGEiÿEXPECTED WAGEi Ciw
Sik Aidli; 4:1
WAGEia EFFORTi Hib Xicei: 4:2
Variable names, descriptions of how each variable used in the estimation of Eqs. (4.1) and (4.2) are constructed, and sample summary statistics are provided in Table 1.
Table 1
Variable names, de®nitions, means, and (standard deviations): Wage and eort equations
Variable name Variable de®nition All Male Female White Black Hispanic WAGE Natural log of hourly wage in 1992 2.17
(0.57) EFFORT Sum of the response to the seven Pearlin
questions used to measure locus of control 6.11 EDUCATION Years of education completed at 1992
interview date EXPERIENCE Weeks of work experience at 1992 interview
date 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 UNEMPLOYMENT Local unemployment rate 0.13
(0.34) UI BENEFITS Average weekly unemployment insurance
bene®t in state of residence in 1992 dollars 165 SMSA 1 if live in an SMSA, 0 otherwise 0.75
(0.43)
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
(0.50) SPOUSE EARNINGS Earnings of spouse in 1992 dollars,
Table 1 (Continued)
Variable name Variable de®nition All Male Female White Black Hispanic CHILDREN Number of children in household 1.10
(1.20) PART-TIME 1 if usually work less than 30 hours
per week, 0 otherwise ASSETS total value of ®nancial assets in 1992 12751
(37975)
MALE 1 if male, 0 otherwise 0.53
(0.50) BLACK 1 if black, 0 otherwise 0.27
(0.44)
0.27 (0.44)
0.27 (0.44) HISPANIC 1 if Hispanic, 0 otherwise 0.19
(0.39)
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
0.78
Average highest grade completed by respondent's parents RELIGION 1 if aliated with any religious group,
ESTABLISHMENT SIZE
Number of employees at establishment 538 (2240)
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
(0.34) NORTHEAST 1 if lived in Northeast region, 0 otherwise 0.16
(0.37) NORTH-CENTRAL 1 if lived in North Central region,
0 otherwise WEST 1 if lived in Western region, 0 otherwise 0.21
(0.41) IMILLS Selection correction term 0.20
(0.25)
n Number of observations 5579 2933 2646 3013 1509 1057
4.2.1. Eort 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'smotives 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 sub-stantial 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 so-cialization on the formation of motives. Measures of PARENT EDUCA-TION, 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 ofmotives to subsequentself-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: Eort 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
aEort,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.
In our view a worker receives an eciency wage when they are earning a WAGEigreater than the wage they expect to earn, EXPECTED WAGEi. In
prior studies (Leonard, 1987; Krueger & Summers, 1988) the wage premium expected to induce greater eort is measured by the dierence 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 dier from those of the average person in their occupation. A person earning an eciency wage would ®nd job loss to be especially costly. Thus, individuals who receive an EFFICIENCY WAGE,
WAGEiÿEXPECTED WAGEi>0, are expected to monitor internally to
a greater extent and to oer their employer greater eort.
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 UNEMPLOY-MENT rate, which portends longer spells for those who become jobless, will prompt greater eort 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 eort on the job. Residents of states with more generous unemployment insurance, greater UI BENE-FITS, face a smaller cost of job loss and are presumed to extend less eort at work.
Unemployment generates ®nancial and psychological hardships (Gold-smith, Veum & Darity, 1996b). These consequences of unemployment are likely to be more vivid or salient for persons who in the past have been ex-posed to UNEMPLOYMENT BOUTS more often and have experienced greater UNEMPLOYMENT DURATION. Therefore, greater personal ex-posure to joblessness may enlarge the perceived costs of unemployment leading to more eort in an attempt to prevent experiencing unemployment again. Alternatively, individual's with unemployment in their past may be-come helpless and fatalistic, believing that the likelihood of experiencing unemployment in the future is independent of their current level of eort on the job. If this were the case, workers with more and longer bouts of un-employment in their past may choose to give less eort than comparable employees with better labor market histories. Hence, the impact of prior unemployment on current eort levels is ambiguous.
oer their employers less eort. It is possible also that workers with more human capital secure jobs they enjoy and are attached to leading them to oer their employers greater eort. Measures capturing these dierent as-pects 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 by EXPERIENCE.
Job loss is costly for workers who possess non-transferable or ®rm speci®c skills, leading those with non-transferable skills to give greater eort 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
de-scribed 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 eort 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 eort. Younger workers who have yet to discover this level may provide more eort, to be viewed as oering an adequate level of job performance.14
Membership in a UNION reduces the probable costs of job loss by pro-viding ®nancial bene®ts and job location assistance. Part-time jobs are usu-ally 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 eort. On the other hand, PART-TIME employees may provide extraordinary eort to enhance their likelihood of being oered a full-time position when one be-comes available.
Job loss is probably viewed as particularly burdensome to people with more CHILDREN. The responsibilities associated with child rearing are expected to inspire greater eort. As SPOUSE EARNINGS rise the
per-14Because 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.
ceived costs of job loss fall and, most likely, eort. 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 dicult 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 eort than otherwise equivalent white em-ployees do, while MALE workers are expected to exert less eort relative to women.
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-ecacy and motivation. Individuals who grew up in households that were aliated with a RELIGION are likely to have developed coping skills and strategies that contribute to self-ecacy or eort.
Firms can use external monitoring to extract greater eort from their work force. However, as the number of employees at a work site expands, it be-comes more dicult to detect a worker's intensity on the job. Therefore, greater ESTABLISHMENT SIZE may diminish worker eort. 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 eort. Firms with MULTIPLE LOCATIONS or work sites, particularly if they are LARGE MULTIPLE LOCATIONS, oer more opportunities for professional advancement. Workers identi®ed as giving greater eort are more likely to be granted transfer promotions. Thus, in-dividuals employed by such ®rms are expected to engage in more internal monitoring and to extend greater eort 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 eort from workers controlling for the level of external monitoring. MANAGEMENT, PROFESSIONAL and CRAFT positions may oer these desirable work characteristics relative to LA-BORER jobs. Thus, the eort equation includes dummy variables that identify occupation of employment. To account for the possibility that worker eort varies systematically across industries, ceteris paribus, dummy variables for industry of employment also are included in the effort equation (Eq. (4.1)).
4.2.2. Wage equation
Eq. (4.2) stipulates that individuals who expend greater eort, ei, and
possess more human capital,Hi, command a higher real wage. The vectorXi
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 aected 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) oer evidence that signi®cant regional wage dierentials exist for otherwise com-parable workers. These pay dierences may re¯ect cultural and institutional variation in setting pay scales in internal labor markets, and incomplete re-sponses 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 re-gions of the US.
A person's WAGEirelative to their expected wage, EXPECTED WAGEi,
appears in the eort equation (4.1), and EFFORTi is included in the wage
equation (4.2). This accounts for thejointdetermination of both WAGEiand
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 EDU-CATION, 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 (eort) diers from the equation Frantz uses to explain locus of control (self-con®dence), since we are estimating a model to test the eciency wage hypothesis. Thus, in our model eort depends on factors in¯uencing the cost of job loss such as; earning an eciency wage (ie. a wage greater than expected), educational accumulation, personal unemployment history, and the generosity of unemploy-ment bene®ts, which are not included in the attitude change equation estimated by Frantz.
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 vari-ables in the system by OLS. Using the coecient estimates from these reduced form equations, we create estimated values of the endogenous variables or instruments.16The estimated values of WAGEiand EFFORTi, are denoted
as PREDICTED WAGEi and PREDICTED EFFORTi respectively.
In Stage II, PREDICTED EFFORTi, which is uncorrelated with ei, the
wage equation error term, replaces EFFORTi ± which is correlated withei ±
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 dierence between WAGEi and PREDICTED WAGEi isei, 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 eciency wage in Eq. (4.1). A standard as-sumption when estimating equations simultaneously is that cross equation error terms are uncorrelated. Thus, we assume thateiis uncorrelated withli,
the eort 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 coecients will suer from selectivity bias. Following Heckman, a selection±correction variable (IMILLS) is included in Eq. (4.2), the wage equation. 18Since the unobservables that inspire a person
16
It might be argued that using a nonlinear estimation technique is more appropriate given that EFFORTias 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 coecients 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).
18As 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 coecients 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.
to participate in the labor force are factors that are likely to also improve eort, (IMILLS) is included in Eq. (4.1), the eort 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 eort equation appear in Table 3. Table 4 presents our estimates of the structural wage equation. 19
5.1. Eort
The results in Table 3 indicate that receiving a greater EFFICIENCY WAGE signi®cantly enhances a worker's eort for each of the data sets. Thus, we ®nd evidence consistent with the eciency wage hypothesis.20This ®nding is also consistent with stress process theory ± unexpectedly high earning re-duce life stresses and enhances self-ecacy and hence eort. To explore whether the impact of earning an eciency wage on eort varies by industry and occupation, we estimated Eqs. (4.1) and (4.2) separately for each of the 10 one-digit industries and eight one-digit occupations. The results are reported in Tables 5 and 6, respectively. Eort is signi®cantly related to receipt of an eciency wage for workers in six of the 10 industries. In the remaining in-dustries eort is independent of earning an eciency wage. The eciency wage inspired signi®cantly greater eort for workers in only three of the occupa-tions, for service workers, operatives, and professional-technical employees.
A rise in the local UNEMPLOYMENT rate induces greater workplace eort for the average person in the sample. Surprisingly, eort is independent of the provision of more generous UI BENEFITS except for black em-ployees, who contrary to expectations, gave greater eort in states where unemployment insurance provisions make the costs of job loss relatively low.
19
The reduced form estimates do not account for the contribution of eort to wages or eciency wages to eort ± they simply account for the in¯uence of exogenous factors on eort and wages. Therefore, the reduced form estimates are unable to oer new insights into wage and eort determination. As a result, they are not reported, but are available from the authors upon request.
20Receiving an eciency wage also enhances a person's relative earnings which Clark and Oswald
(1996) ®nd contributes to subjective happiness.
Table 3
Structural ordered probit eort estimatesa
Variable name (expected sign)
All Male Female White Black Hispanic
EFFICIENCY
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) UNEMPLOYMENT
BOUTS (?)
ÿ0.14eÿ01 (2.50) ÿ0.13eÿ01 (1.60) ÿ0.15eÿ01 (1.67) ÿ0.18eÿ01 (2.16) ÿ0.20eÿ01 (1.73) ÿ0.32eÿ02 (0.25)
UNEMPLOYMENT DURATION (?)
ÿ0.19eÿ02 (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
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) SPOUSE
EARNINGS (ÿ)
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) ESTABLISHMENT
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)
Table 3 (Continued) Variable name (expected sign)
All Male Female White Black Hispanic
MULTIPLE LOCATIONS (+)
0.19eÿ03 (1.00) 0.72eÿ01 (1.31) ÿ0.91eÿ01 (1.54) ÿ0.69eÿ01 (1.27) 0.88eÿ01 (0.11) 0.17eÿ01 (1.77)
LARGE MULTIPLE LOCATIONS (+)
ÿ0.28eÿ01 (0.68) ÿ0.80eÿ01 (1.38) 0.40eÿ01 (0.68) ÿ0.35eÿ01 (0.61) 0.19eÿ01 (0.24) ÿ0.89eÿ01 (0.94)
PROFESSIONAL PARENT (+)
ÿ0.67eÿ01 (1.61) ÿ0.36eÿ01 (0.61) ÿ0.11 (1.86) ÿ0.54eÿ01 (1.04) ÿ0.95eÿ01 (0.97) ÿ0.45eÿ01 (0.39)
BOTH PARENTS (+)
0.18eÿ02 (0.48) ÿ0.63eÿ02 (0.12) 0.47eÿ01 (0.85) ÿ0.25eÿ01 (0.39) 0.49eÿ01 (0.83) 0.39eÿ01 (0.45)
PARENT EDUCATION (+)
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] aAll equations include INDUSTRY and OCCUPATION dummy variables (t-statistics in parentheses).
*Statistically signi®cantly dierent from zero at the 0.1 con®dence level. **Statistically signi®cantly dierent from zero at the 0.05 con®dence level. ***
Statistically signi®cantly dierent from zero at the 0.01 con®dence level.
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Table 4
Structural OLS log wage estimatesa
Variable name (expected sign)
All Male Female White Black Hispanic
PREDICTED EFFORT (+)
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)
ESTABLISHMENT 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)
MULTIPLE LOCATIONS (+)
0.31eÿ01 (1.98) ÿ0.93eÿ02 (0.38) 0.71eÿ01 (3.00) 0.95eÿ01 (4.25) 0.31eÿ02 (0.11) ÿ0.10 (2.37)
LARGE MULTIPLE LOCATIONS (+)
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)
UNEMPLOYMENT (ÿ)
ÿ0.49eÿ01 (2.44) ÿ0.30eÿ01 (1.02) ÿ0.50eÿ01 (1.88) 0.83eÿ01 (3.07) ÿ0.21 (3.45) ÿ0.16 (4.20)
UNEMPLOYMENT BOUTS (ÿ)
0.18eÿ02 (0.68) ÿ0.32eÿ02 (0.90) ÿ0.75eÿ03 (0.20) 0.11eÿ01 (2.88) 0.67eÿ02 (1.33) 0.13eÿ01 (2.42)
UNEMPLOYMENT DURATION (ÿ)
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 (4.21)
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
(2.57)
0.26eÿ02 (0.29) 0.23eÿ02 (0.23) 0.20eÿ01 (1.33)
Table 4 (Continued)
Variable name (expected sign)
All Male Female White Black Hispanic
NORTHCENTRAL (?)
ÿ0.60eÿ01 (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 dierent from zero at the 0.1 con®dence level.
**
Statistically signi®cantly dierent from zero at the 0.05 con®dence level.
***Statistically signi®cantly dierent from zero at the 0.01 con®dence level.
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Table 5
Estimated eect of eciency wage on eort, and eort on the wage: by industrya
(1) (2) (3) (4) (5)
INDUSTRY (Sample size) o EFFORT
o EFFICIENCY WAGE
o Wage
o Effort Wage equation industry
dummy variables: Krue-ger±Summers
Wage equation industry dummy variables: Gold-smith±Veum±Darity
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 coecients 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 coecient estimates are suppressed. Columns 4 and 5 report coecients estimated on industry dummy variables from a wage equation estimated with data on workers regardless of industry in which they are employed. Column 5 presents the coecients on the industry dummy variables using the entire NLSY sample (Table 4, column 2). Krueger and Summers use data drawn from the May 1984 Current Population Survey. For Krueger and Summers a positive (negative) and signi®cant coecient reveals an industry that on average, pays wages above (below) the mean for all industries (Krueger & Summers, 1988, Table 1, p. 264). EFFICIENCY WAGE(WAGE)PREDICTED
WAGE)eidisturbance term in Eq. (4.2) (t-statistics in parentheses).
*Statistically signi®cantly dierent from zero at the 0.1 con®dence level. **Statistically signi®cantly dierent from zero at the 0.05 con®dence level. ***Statistically signi®cantly dierent from zero at the 0.01 con®dence level.
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351±385
Table 6
Estimated eect of EFFICIENCY WAGE and OCCUPATION on EFFORT, and EFFORT on the WAGE: by OCCUPATIONa
(1) (2) (3) (4) (5) (6)
OCCUPATION (sample size) o Efficiency Wageo EFFORT
o WAGE
o EFFORT Wage equation
occupa-tion dummy variables: Krueger±Summers
Wage equation occupa-tion dummy variables: Goldsmith±Veum±Darity
Eort equation occupa-tion dummy variables: Goldsmith±Veum±Darity PROFESSIONAL &
TECHNICAL (1016)
0.20 (2.30) 0.48 (2.64) 0.21 (11.72) 0.16 (4.63) 0.16 (2.04)
MANAGEMENT & ADMINISTRATION (631)
0.68eÿ01 (0.56) 1.09 (4.93) 0.22 (10.80) 0.10 (2.76) 0.23 (2.87)
SALES (258) 0.17 (0.88) 0.11 (0.28) 0.15 (6.17) 0.67eÿ01 (1.50) 0.22 (2.22)
CLERICAL (1047) 0.12 (1.17) 0.75 (4.55) 0.30eÿ02 (0.04) 0.18eÿ01 (0.58) 0.11 (1.41)
CRAFTS (657) 0.83eÿ02 (0.08) 1.09 (3.62) ÿ0:34eÿ01 (2.13) 0.75eÿ01 (2.17) 0.19 (2.56)
OPERATIVES (720) 0.39 (3.36) 0.63 (2.34) ÿ0:17eÿ01 (1.57) 0.71eÿ01 (2.45) 0.47eÿ02 (0.07)
SERVICE WORKERS (843) 0.37eÿ01 (0.44) 0.26 (0.87) ÿ0:66eÿ01 (3.88) ÿ0:20e-01 (0.66) 0.94eÿ01 (1.24)
LABORERS (407) 0.19 (1.66) ÿ0:29 (0.54) ÿ0:15 (7.68) Control Control a
The coecients reported in columns 2 and 3 are extracted from estimates of Eqs. (4.1) and (4.2) using data on a particular occupation±occupation is identi®ed by row. The remaining coecient estimates are suppressed. Columns 4 and 5 report coecients estimated on occupation dummy variables from a wage equation estimated with data on workers regardless of occupation in which they are employed. Columns 5 and 6 present the coecients on the occupation dummy variables using the entire NLSY sample (column 5 is identical to column 2 of Table 4). Krueger and Summers use data drawn from the May 1984 Current Population Survey. For Krueger and Summers a positive (negative) and signi®cant coecient reveals an occupation that on average, pays wages above (below) the mean for all occupations (Krueger & Summers, 1986, Table III, p. 11) (t-statistics in parentheses).
*Statistically signi®cantly dierent from zero at the 0.1 con®dence level. **Statistically signi®cantly dierent from zero at the 0.05 con®dence level. ***Statistically signi®cantly dierent from zero at the 0.01 con®dence level.
376
A.H.
Goldsm
ith
et
al.
/
Journal
of
Economic
Psychology
21
(2000)
Proponents of eciency wage theory believe individuals who are more familiar with the ®nancial and psychological costs of unemployment, due either to being exposed to more UNEMPLOYMENT BOUTS or having faced greater UNEMPLOYMENT DURATION, will perceive a relatively high cost of job loss leading to greater eort. However, for the full sample, such individuals oered signi®cantly less eort. Adherents of the life stress theory in psychology would anticipate these results, which are likely to puzzle economists. These ®ndings suggest that personal experiences with unem-ployment generate life stresses fostering a sense of helplessness or despair that hampers an individual's motivation.
Persons who have accumulated more transferable human capital are ex-pected to be low eort employees, since they are likely to face less diculty ®nding comparable work. However, our ®ndings reveal that individuals with more EDUCATION, and those who have acquired greater verbal and mathematical skills ± those with higher AFQT scores ± give greater eort on the job. One possible explanation for this set of ®ndings is that individuals with greater formal education and those with greater knowledge oer more eort since they are better able to locate positions where they enjoy their work. Eort is unrelated to the extent of work EXPERIENCE and the amount of JOB TRAINING accumulated, two other forms of human capital, for the typical individual in the sample. However, individuals with greater TEN-URE, those who have had the opportunity to acquire greater informal hu-man capital, exhibit lower levels of eort. High TENURE individuals may believe that a disproportionate share of the skills they acquired via tenure are general or transferable skills, rather than ®rm speci®c or non-transferable. If this were the case, greater tenure would reduce the cost of job loss and provide an incentive to curtail eort.
Workers of greater AGE give signi®cantly less eort, possibly due to the accumulation of transferable social capital such as maturity and wisdom. Unionized workers are expected to perceive a lower cost of unemployment leading to less workplace eort. However, UNION membership does not in¯uence eort. Job loss is likely to be viewed as less damaging by PART-TIME employees. Indeed, a typical PART-PART-TIME employees oers a lower level of eort.
CHILDREN may have needs that interfere with workplace obligations and, may present challenges that are stressful to address, which are likely to inhibit eort. Children may also be a source of joy that diminishes stress leading to enhanced motivation. Our ®ndings suggest that eort is unrelated to the presence of CHILDREN.
Individuals with greater ®nancial ASSETS and persons with larger SPOUSE EARNINGS are likely to face lower perceived costs of job loss. Nevertheless, they exhibit signi®cantly more eort. Having less constraints may allow such persons to shop for work until they ®nd a position where they self-monitor to a greater extent than persons with lower ®nancial sup-port. An alternative explanation is that such persons face fewer life stresses leading to greater self-ecacy and eort.
BLACK employees provide a greater level of eort than white workers, who provide the same level of eort as HISPANIC employees. MALE workers exhibited a greater level of eort than females, although we postu-lated that women should perceive a higher cost of job loss leading them to provide more eort than comparable males. It could be that social stigma associated with unemployment is greater for males and must be considered when evaluating gender dierences in the perceived cost of unemployment.
MARRIED individuals have a signi®cantly higher level of self-ecacy than do comparable unmarried persons. This ®nding is consistent with marriage providing social support that diminishes life stresses and contrib-utes to greater productivity. Persons who as youths were members of families aliated with a religious organization may acquire important coping skills, but they do not exhibit signi®cantly greater self-ecacy.
Individuals who as 14 year olds had more highly educated parents (PARENT EDUCATION) are characterized by greater eort when em-ployed later in life. This ®nding is consistent with more educated parents being better able to help their children acquire motives or dispositions to strive to meet their objectives. Other variables (eg., PROFESSIONAL PARENT, BOTH PARENTS) expected to contribute to the formation of motives were not signi®cant determinants of self-ef®cacy or motivation.
Workers employed at a larger ®rm, greater ESTABLISHMENT SIZE, do not provide their employer with signi®cantly less eort. Moreover, our ®ndings reveal that an employee's eort level is unaected by whether their employer has MULTIPLE LOCATIONS or LARGE MULTIPLE LOCA-TIONS. The sample selection correction (IMILLS) is insigni®cant, implying no correlation between unobservables that in¯uence the decision to partici-pate in the labor force and a person's level of eort if employed.
Eort is signi®cantly greater for workers holding MANAGEMENT, PROFESSIONAL and CRAFT positions than for employees in LABORER jobs, controlling for receipt of an eciency wage (see Table 6, column 6). This ®nding suggests that eort depends on job characteristics aside from compensation.
In sum, the key ®ndings are that workers who receive a greater EFFI-CIENCY WAGE provide their employers with more eort. Also, ``fear'' produced by a larger local rate of UNEMPLOYMENT enhances eort. These ®ndings are consistent with the predictions derived from the shirking version of the eciency wage hypothesis.
5.2. Wages
The results in Table 4 indicate that PREDICTED EFFORT is positively and signi®cantly related to the real wage for each of the data sets.21 Moreover, the coecients estimated on the one-digit industry dummy variables contained in the wage equation reveal a pattern of interindustry wage variation similar to that reported by Krueger and Summers (1988)22 They speculated that in industries where external monitoring is costly and ineective eort will be low, high wages are paid to induce internal monitoring that in turn leads to more eort. Thus, in their view, high wage industries are high eort industries. But, we ®nd that when eort ± which in¯uences the wage ± is held constant, the high wage industries identi®ed by Krueger and Summers (1988) are still high wage industries (Table 5, columns 4 and 5). Something other than superior eort is re-sponsible for the payment of high wages in these industries. Our ®ndings deepen the puzzle that Krueger and Summers (1988) thought they had solved, of why employers in some industries pay higher wages for com-parable workers.
Greater EFFORT contributes signi®cantly to a worker's WAGE for workers in seven of the 10 one-digit industries examined (Table 5, column 3). In the remaining industries a person's WAGE is independent of their level of
21
Andrisani (1978) reported that persons with a more internal locus of control earn signi®cantly higher wages when locus of control is treated as an exogenous variable. Duncan and Morgan (1981) later replicated his work. See Goldsmith et al. (1999) for a review of the empirical work on the link between locus of control and wages in which locus of control is assumed to be an exogenous variable.
22
The wage equation estimated by Krueger and Summers (1988) included a dummy variable for six one-digit industry classi®cations for which we included a corresponding dummy variable in the wage equation we estimated. Both studies ®nd that workers in Wholesale±Retail Trade are relatively low paid, and that Construction and Transportation workers are relatively high paid. Still, two dierences have emerged. We ®nd Agriculture±Mining workers earn wages comparable to manufacturing workers while Krueger and Summers ®nd they earn signi®cantly more than manufacturing employees. They also ®nd Finance industry employees are the recipients of relatively high pay, while they earn comparable pay to manufacturing workers in our analysis.
EFFORT. Workers in ®ve of the eight one-digit occupations receive a wage that is signi®cantly greater when their level of eort rises (Table 6, column 3). The relation between eort and pay is strongest (e.g. largest coecient on the measure of eciency wage) for workers holding MANAGEMENT AND CRAFT positions ± occupations ordinarily characterized by more symbolic-analytical work or so-called ``thought work''.
The coecient estimates on three of the ®ve dierent types of human capital, (EDUCATION, EXPERIENCE, TENURE) are positive and sig-ni®cant for the typical individual in the sample. JOB TRAINING, AFQT, and AGE have no impact on a worker's wage level when eort and human capital are controlled for. UNION membership and working at a larger ESTABLISHMENT SIZE enhances a person's wage signi®cantly. In addi-tion, employees of ®rms with MULTIPLE LOCATIONS or plants, including those with LARGE MULTIPLE LOCATIONS, receive signi®cantly higher wages.
Full-time employees earn wages that are virtually equivalent to those earned by their PART-TIME counterparts. Workers in larger labor markets ± bigger SMSA's ± earn signi®cantly more per hour. A weak local labor market, due to a rise in the local rate of UNEMPLOYMENT, signi®cantly reduces the wages workers receive. A person's wage is independent of prior UNEMPLOYMENT BOUTS. An interesting result is that wages are greater for persons with a larger cumulative amount of time spent unemployed. If the unemployed used their time to ®nd jobs that better match their skills and interests ± jobs where they are more productive ± then this ®nding would be expected.
Black employees, with comparable skill characteristics, earn signi®cantly less than whites when the joint determination of wages and eort is taken explicitly into account. We obtain this result with AFQT include as an ex-ogenous variable.23 In contrast, Neal and Johnson (1996) using the NLSY ®nd that when the AFQT is included in the wage equation, as a measure of basic skills, the gap between white and black wages declines dramatically and becomes statistically insigni®cant. One possible explanation for their ®nding is that the dummy variable identifying black employees con¯ates two eects on the wage rate;discriminationby employers that is expected to reduce black employee wages and the relatively high level of effort black employees are
23Rodgers and Spriggs (1996) suggest that it may be inappropriate to treat the AFQT as an exogenous
variable in a wage equation.
