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Characteristics of blue collar and white collar jobs and pecuniary returns
CHARACTERISTICS OF BLUE COLLAR AND WHITE JOBS AND PECUNI.‘lRY RETURNS Nguyen
COLLAR
T. Quad
Within the past decade, there have been numerous studies that attempt to estimate "prices" for nonwage task characteristics and to test the theory of equathat individuals lizing differences: are willing to accept less pleasant jobs provided that they receive compensating The differences in their wage rates. majority of those studies concentrate primarily on the effects of undesirable environmental working conditions, such as noise, weather, hazardous equipment, etc. . . . on wage rates, and very few of them have looked into the job itself, specifically on how the task is performed.2 Differences in wages among individuals are not only due to pecuniary and cognitive attributes but also to the characteristics of the task performed by the individual in the course of his or her job. However, the question of occupational attitude and work interaction between blue collar and white collar workers within a rigid hierarchical organization, and their expectations about task performance, has never been raised by researchers except by students of organizational behavior.
and other worker characteristics, physical aspects of the job do support the concept of equalizing differences and their effects differ for blue and white collar workers. The plan of the paper is as follows: Section I provides a simple model of blue collar and white collar workers behavior along with a short review of the empirical literature; Section II investigates the types of physical working conditions that may influence the wage structure in general and those of the blue-collar and white-collar occupations in particular; Section III presents a discussion of the empirical results followed by the conclusion.
I.
The Literature
and a Model
The theoretical literature on the hedonic aspect of prices for nonwage characteristics coalesced with the work of Rosen [1974] and R.E.B. Lucas [1977]. The theory was testedbyLucas [1977] with an hedonic wage equation: given the level of schooling, Lucas incorporates in the wage equation proxies for nonsedentary, repetitive and supervisory asphysical, pects of jobs. His results show that jobs which require lifting and physical exertion are not rewarded. In contrast, repetitive jobs and physical conditions of the workplace significantly explained small percentagesofthe increase in wage. Bluestone [I9741 and Quinn [1975] also investigate, via two different micro data sets, the effects of such physical characteristics of jobs on hourly wage. The
This paper will attempt to fill the void, first by proposing a model of worker behavior within the context of firm organization, and second, by investigating some of the tasks performed by blue and white collar workers and specifically their distaste toward certain reflected by job characteristics as The principal variation in earnings. finding of this paper is that, while controlling for personal, demographic
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authors provide contradictory results. In the former study, a job which requires physical strength has a significant but negative effect on wage, while in the latter the reverse is true. In both studies, bad working conditions, i.e., the environmental aspect of the workplace, have no significant effects. Duncan and Stafford [1980] have extended the basic model of compensating differential to include the role of unions on the pace of work. They conclude that unionized blue-collar workers enjoy large wage increases but at the expense of declining desirable working conditions. However, they fail to obtain significant compensating differentials for white-collar workers. Smith [1979] comments in a recent survey, that failure to provide convincing evidence of compensating wage differentials is due to problems of comparability across individuals in self-reported data and to problems of describing disagreeable working conditions due to heterogeneous tastes. Further, Hamermesh [I9771 argues that a worker perception of badness in the work place tends to improve with rising and specific training and, in wage general, high wage occupations have better working place and contented workers. Hence there is a strong degree of simultaneous bias in the estimation working conditions of self-reported measures and level of earnings. Brown [1980] in an effort to minimize the problem of omitted variables and measurement error that could have engendered bias in the estimated coefficients, uses a first difference specification so that ability, which is measured at two points in time, is unrelated to both the change in earnings and the change in working conditions. He showed no empirical evidence of compensating
XV, No. 1 & 2
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difference. In contrast, Duncan and Holmlund [1983], and Saffer [1984] provide empirical results that support the compensating difference hypothesis. In the former study, two points in time changes in self-reported job characteristics are used as regressors for changes in relative wages, while in the latter study a single comprehensive working condition variable is used via a system of simultaneous equations and generalized probit technique. However, Saffer shows that the first difference specification will not eliminate the bias inherent in self-reported data. This is one of the reasons that the Duncan and Holmlund results show many estimated coefficients with the "wrong" signs. Although Saffer's sophisticated approach may provide efficient estimates, the construction of a single unobserved latent variable as a proxy for working conditions does not enlighten the reader as to the complexities of the job tasks. Model Consider a firm that employs n1 number of blue-collar workers or production and ng number of white-collar workers, workers or nonproduction workers. Each group of workers performs a well defined but different set of tasks in their job assignment. Moreover, one can presume that, in the context of firm operations, white collar workers actively promote their expertise onto the blue collar workers. For example, the white collar group could decide on various production techniques or on the purchase of capital equipment that would constrain the blue collar group to have specific workerSecond, white machine arrangements. collar workers are loathe to carry Out tasks usually assigned to blue collar
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Sitting at a desk and writing workers. directives is preferred to assembling machinery components at a specific speed. Third, white collar groups will attempt to maintain their preferred status quo in the organization. Workers'
Choices
We posit that task performance is an argument of the utility function, and that "dissatisfaction (results) from performing a task that involves the use of of an ability than is more, or less, possessed by the person" [Lucas, 1977, p. 5503. Thus we can presume that a person with managerial ability but of low strength may enjoy being an administrator, and the same person may be repulsed by the prospect of fast paced mechanical work (at the same wage rate). We also assume that wages vary across jobs and workers. Further, because of innate ability and work assignment rigidity, not every job is included in the worker's there will be a choices set. However, wage rate compatible with the excluded jobs. Jobs within the white collar Occupations are excluded from the blue collar choice set. Here we assume that blue collar workers are ensconced in their occupational jobs and, at least in the short run, mobility toward a white collar assignment is restricted, Since explicit and implicit costs of training are prohibitive. Conversely, white collar workers will not yearn for blue collar jobs. If, for one reason or another, a white collar worker has to Perform a blue collar task to which he/she is averse, then he/she demands compensation.
both
We also about
postulate that the pecuniary
workers rewards
care from
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his work and about the "quality" of his/her job. Quality may be described by some vector of attributes of the job performed, denoted herein as vector Zi for job i. Attitudes to pecuniary and non-pecuniary rewards vary from worker to worker, however, it is safe to assume that tastes are partially conditioned by some vector C of measured personal characteristics of the worker. Within the blue collar worker choice set, we have the following worker's utility function with hedonic factors.
Uk = ui(wbf, where
WEi is the kth worker
wwi 9 zbi s Ck) blue collar on the ith
W,,i is the white on the ith job Zbi tics
(1)
wage for job
collar
the
wage paid
is the Vector of characterisfor the ith blue collar job
Ck is the acteristics On the other worker's utility as
vector of personal for worker k
char-
hand, the white collar function can be written
Ui = Uw(&s wbi, ZwisCk)
(2)
where the w subscript denotes white collar worker and Wbi is the blue collar wage paid for the ith job. It should be emphasized that Wwi and Wbi are wages that are based on personal characteristics such as skill and tastes, and are not influenced by job attributes. The above discussion brings forth the followa) in equation ing formal assumptions: (l), both Wii and white collar wage Wwi
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have a positive effect on Ub. Even though the hourly wage rate found in some white collar jobs is lower than most blue collar jobs, workers in blue collar occupations tend to regard white collar occupations to be "better" than their own. That is, white collar jobs induce a "positive within the firm's taste." For example, rigid organizational framework, an engineer has more privileges than a technician,eventhoughthefonner's hourly wage maybeless than the latter's. Conversely, in equation (2) unless the blue collar hourly wage is higher than the white collar wage to offset any disutility associated with blue collar job attributes, Ubi could have no effect, or even a negative effect, on the white collar preference function; b) although the total effects of Zwi, the vector of white collar job characteristics. on U, is unknown (obviously an unpleasant task should be compensated), the effect of a distasteful blue collarjob'sattributes zbi will be stronger on Uw than on Ub. That is, white collar workers are more averse to blue'collar job attributes than blue collar workers to their own jobs. To induce white collar workers to hold jobs that have blue collar attributes, the former must be compensated accordingly; c) the level of compensation depends on the blue and white collar wage difMore specifically, the wage ferential. earned by a blue collar worker on job i should be greater than the one earned by a white collar worker on that same job. If both wage levels were the same, and given the aversion of white collar workers to unpleasant conditions, then in order to keep the latter on the same indifference curve, they will have to hold blue collar jobs that have a lower degree of unpleasantness; d) the status quo aspired by white collars will be
XV, No. 1 & 2
modeled herein as a predetermined level $. A blue collar worker will attempt to optimize his/her utility subject to the white collar constraint U2, e) the measurable personal characteristic variable Ck that could be constructed as a distinguishing feature between blue and white collar workers is the years of schooling. Graphically the model assumes that workers choose from a set of jobs bounded by a frontier such as FlFl illustrated in Figure 1. Given a cormK)n wage offered to a group of workers who have a distaste for a certain job characteristic, whether they are blue or white collars, and given complete information and various tastes, efficient job choosers distribute them selves along FlFl at points where their indifference curves are tangential to the frontier. Each coaaaon wage offer has a positive slope given a universal distaste for that job characteristic. The intercept b/b of the wage line measures both the pecuniary and the nonpecuniary aspects of wage for blue collar worker k with indifference curve Ub. Ub is affected only by personal tastes and not bY Similarly some job characteristics. Figure 1 can also be used to depict white collar workers' behavior by relabelling the axes to white collar wage and white However, if collar job characteristics. a white collar worker were to take a job with blue collar attributes, his aversion to the job will make the rate of substitution between his wage and that distasteful blue collar job characteristic higher than the substitution between the blue collar wage and its own job characteristic. In terms of a graph relating wages and blue collar job characteristics, a white collar worker's indifference curve has a stronger curvature than
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FIGURE
blue
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1
Wage. 4 collar
‘b
. Blue collar job characteristic
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FIGURE
2
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2
Wage
"b
.
.
job
Blue collar characteristic
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the one for blue collar worker. This is shown in Figure 2 where the vertical axis is the wage level for both groups of workers, and the horizontal axis is the blue collar job characteristics. To maintain the white collar indifference curve at a higher level than the blue collar, and given that white collar workers have a stronger distaste for a blue collar job attribute, the slope of the wage line tangent to the white steeper collar indifference curve is than the one for blue collars. The resulting equilibrium points between white and blue collar indifference curves and their respective wage lines at a given blue collar job characteristic are shown in Figure 2 as D and A, respectively. It should be noted that points D, A, and z are lined up on the same hyperplane. The slopes of W,D and WbA represent the job attribute affected wages for white and blue collar workers, respectively. To induce a white collar worker to hold a job with blue collar attribute, the compensation constraint dictates that the taste affected W, be smaller than Wb. These are represented by the interOtherwise, as cepts on the wage axis. illustrated in Figure 3, both white and blue collar worker equilibrium points with their respective indifference curves will not be lined up on the same hyperplane. The tangency point between U, and the wage line is at F rather than at D. In this case an uncompensated white collar worker is willing to perform a blue collar task only if the degree of disamenity is less. It should be pointed Out that for white collar workers whose jobs are far removed from the plant or the shop floor, then both blue and white collar utility sets are mutually exclusive, comparative analysis as hence suggested herein will be difficult.
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Figure 2 also provides an extension to the above analysis. If the white collar worker were to be paid the same job attribute affected wage as the blue collar worker, then the former, in order to maintain the same level of utility, will substitute for more pleasant jobs such as at point E. In this case the wage line Wb' is parallel to Wb. The same can be said for point G in Figure 3. Workers maximize their job choices by equating the marginal rates of substitution between wage rate and each job characteristic with the ratio of their shadow prices at the relevant point on the efficiency frontier F~FI.~ The resulting optimization provides the following set of hedonic wage equations
II.
wbki = w(zbis
&is
Ck)
(3)
w:f = w(zbi.
&is
Ck)
(4)
Vwlrbles
and Data
The theory on hedonic wage equations does not provide any guidance in the choice of functional forms. A convenient specification for the estimation of wage differentials is a regression of the natural logarithm of average hourly earnings on a set.of dunvny variables representing population characteristics, human capital indicators, industrial and occupational characteristics.4 Job Characteristics The problems of working environment and task performance have been primarily scrutinized by industrial psychologists.5 For example, LaFitte Cl9581 has observed that some skilled workers perform well and are satisfied with their jobs while
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XV, No. 1 i?i 2
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FIGURE 3
'b
* Blue collar job characteristic
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others do the minimum required by their employers and report themselves as dissatisfied with their jobs. These researchers also point out that this dichotomy is significantly associated with the observed dichotomy among tasks between clean and light, or between heavy and dirty. In other words, workers regard themselves as well off and are highly cooperative and highly productive when they appreciate the superior physical conditions of their occupation. This positive relationship between worker satisfaction and working conditions may be translated into fluctuations in earnings due to bonus payment or discretionary pecuniary awards, for example for good time keeping or saving of materials. However, because incentive payment may have to be provided in order to keep qualified workers satisfied and productive, it is difficult to render generalfzations on the effectiveness of economic incentives without looking at the task itself. As discussed by Davison, et al response to incentive [1958], workers' payment will vary according to the degree of skill and concentration involved. Of considerable significance is whether the output is determined by machine or determined by the operator himself, and whether the work is heavy or light, varied or repetitive. Furthermore, the relationship of the operation to previous and succeeding operations is also significant since it requires the worker to have some physical coordination and mental concentration in the production of the output. Hence Davison states that "variations, actual or anticipated, in the flow of work may exert considerable influence on workers' reactions to incentive payment schemes" [Davison, et al., 1958, p. gg]. In this case, fluctuations in earnings brought about by piece rates
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165
or other form of payment by results, depend on worker skill and stamina. Moreover, within the same occupation differences in personal aptitude are often associated with wide differences in the tasks performed. Brown [lg77] points out that the variance of earnings for professional and technical occupations arises from differences in the level of responsibility and also from personal differences at each level: that is, average earnings increase with the level of responsibility but so does the range As job qualification reof earnings. quirements increase, differences in personal ability make more difference to the product. This would provide a higher pecuniary reward than for jobs that can be performed by less qualified persons. In sum, the intrinsic aspects of the job shape workers' attitudes, prompting them to ask for equalizing differences in pecuniary reward. The Data The data and variables come from the Survey of Working Conditions.C Unique to this sample is a set of variables which encompasses workers' descriptions of their working environment and jobs. Out of the set we have chosen five variables which provide a description of the worker's perception of his/her job. From the discussion presented above, we have looked specifically at the pace of the work itself, the physical aspect of the work and the responsibility concomitant The self-reported job desto the job. cription is incorporated into a binary For example, the variable FAST variable. WORK describing a job that requires an individual to work very fast, is assigned a value one if the worker answers in the affirmative, and zero otherwise.Similar-
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ly, binary explanatory variables are also used for jobs which require hard work (HARD WORK) and physical effort (PHYSIand for jobs which allow some CAL), degree of freedom (FREEDOM). The variable FREEDOM is used to describe "a job that allows you a lot of freedom as to how you do your work. In other words, FREEDOM is not meant to describe idleness or free time on the job, but it is a proxy for a job which requires some degree of responsibility from the holder. The variable PHYSICAL is a proxy for manual task where the use of a machine is minimal or nonexistent. Moreover, to take into account the prior proposition that, besides the intellectual skill provided by formal schooling, physical skill may cause, at least in the short run, large wage differences, another binary variable, SKILL, is included in the earnings equation for jobs which are described as demanding some high degree of manual skill.
III.
Findings
The least squares method is applied to a sample of all workers, and separately to the blue collar and white collar subResults are shown in Table 1. groups. In column 1, estimated coefficients for the relevant variables in the overall sample are presented. Columns 2 and 3 tabulate regression results for the blue collar and white collar worker samples, Estimates are classified respectively. such as working condiby categories, tions, industries, occupations. The value of the adjusted R2 is relatively high given the nature of the data set. For 1237 observations, the adjusted R2 is about -53, this is due in part to the inclusion of industry and occupa-
XV, No. 1 & 2
tional variables. As expected, the estimated coefficients for human capital indicators and individual demographic characteristics have the theoretically correct sign and are highly significant. For example, in the all workers sample, an extra year of experience will raise hourly earnings by 1.7 percent, while an extra year of schooling will increase earnings by 6.1 percent. As discussed above, one way to distinguish white collar from blue collar personal attributes istheformer's affinityforschooling. The schooling incremental effects on blue collar and white collar earnings are 4.9 percent and 7.0 percent, respectively. Of greater interest is the marginal contribution of each regression variable in the explanation of the variation in earnings. Thus the experience variable contributes, with significant diminishing returns, about 5.1 percent to the explanation of the variations in earnings given all other regressors.7 This contribution drops to 4.7 percent in the blue collar sample, but increases drastically, up to 88.4 percent in the white collar workers sample. At the same time the education variable contributes about 8.7 percent to the explanation in earnings for the overall sample, while it is 5.8 percent for the blue collar sample and 10.7 percent for the white collar sample. This result reinforces the notion that formal schooling and post-schooling experiences, in contrast to blue collar occupations, are strong elements in explaining variations in earnings within the white-collar occupation. The dichotomy between white and blue collar groups is also apparent among female, unionized, For instance, female and urban workers. workers earn on the average 36 percent less than their male counterparts, with a greater discrepancy in the blue-collar
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sample. As far as residential locations and regions are concerned, workers living in large urban areas have a 17.5 percent edge and southerners earn about 15 percent less than workers in other parts of the country. However, white collar workers in urban areas have higher earnings than any other group. At the same time, they do not suffer financially as much for residing in the South. The estimated relative effect of in the overall unionism on earnings sample is .1239 or a non-union earnings differential of 13.2 percent. The difference in the effects of unionism on earnings is clearly significant when the sample is disaggregated into blue-collar and white-collar. The proportionate differential is about 23.7 percent in the blue-collar category, while it is almost nil in the white-collar.* Variables depicting job characteristics encompass mainly the physical and supervisory sides of a job. The estimated coefficients for variables in the overall sample such as freedom on the job (FREEDOM), job requiring manual skill (SKILL), and job requiring hard work (HARD WORK) are all statistically significant at least at the 10 percent level and have the expected sign.g Freedom on the job provides the worker with some degree of responsibility which implies either intellectual ormanual ability. One expected result is the highly significant, but coefficient for negative, estimated the variable depicting physical effort on the job (PHYSICAL). One reason is that jobs in which physical efforts are needed are generally menial jobs and for which machine utilization is minimal. In contrast the estimated coefficient for the variable FAST WORK is not significant.lO
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The regressions for blue collar and white collar subgroups provide some interesting contrasts, as seen in columns 2 and 3. For the blue collar workers, the coefficient for FAST WORK becomes significant at the 10 percent level, as does SKILL; while the estimate for PHYSICAL is statistically significant at the 20 percent level only. FREEDOM and HARD WORK are not at all significant. In the white collar subgroup, FAST WORK has very little effect on earnings as expected from that group of workers, but coefficients for FREEDOM, HARD WORK, and PHYSICAL do show a significant explained variation in earnings. The first two variables have positive estimated coefficients while the latter is negative. In other words, work pace is best measured by the proxy variable FAST WORK for the blue collar worker to reflect the intensive utilization of capital employers, while it is best accounted for by the proxy FREEDOM in the white collar group to reflectthemore flexible work schedule and the higher degree of responsibility. Moreover, hard work is a disutility for a white collar worker, so that he must be compensated proportionately. Having to provide physical effort is considered In contrast, for a blue collar menial. worker, hard and physical jobs are considered the norms, so that these proxies do not provide any statistical effect on earnings, while acquiring a skill should reflect opportunity costs and obstacles to entry. The inclusion of occupational and industrial variables in the regression may have taken away some of the explanatory power that legitimately belongs to the working conditions variables. However it is safe to state that individual workers in general are restricted in their job search by inadeor sometimes incorrect training, quate,
16::
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Table 1. Regression estimates standard error in parentheses).
Explanatory Variables
All Workers (1)
XV, No. 1 & 2
of In Hourly
Earnlngs.(absoltite
Blue Collar
(2)
value
of
White Collar (3)
Constant
.1052 (.0877)
Union
.1239** (.0249)
Exper
.0170** (.0021)
.0149** (.0027)
.0196** (.0003)
Exper2
-.00027** (.00004)
-.00022** (.00005)
-.00032** (.00007)
Education
.0593** (.OOSS)
.0487** C.0079)
.0672** (.0082)
Female
-.4399** c.0267)
-.5020** (.0379)
-.4277** (.0356)
Black
.0191 t.0361)
.0285 c.0446)
.0571 (.0606)
SnsA
.1610** c.0251)
.1065** C.0344)
.1847** C.0353)
South
-. 1393** (.0290)
-.1584** c.0385)
-.1051** (.0436)
Fast Work
.0350 t.0263)
.0590* c.0358)
.0144 (.0378)
Preedom
.0601** (.0228)
.0328 (.0307)
.0804** C.0334)
Skill
.0894** (.0252)
.1175** (.0325)
.0431 (.0385)
Hard Work
.0479* (.0274)
-.00297 (.0390)
.0911** (.0376)
Physical
-.0820** (.0282)
-.0525 C.0345)
-.0921* c.04771
.1656 (.1158)
.3560 (.1611)
.2129 (0.450)
-.0174 (.0400)
Job Characteristics:
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Industries
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Variables
Construction
.1032 (.0600)
Manufacture
.ObO?
Transport Wholesale/Retail
.1103
.1474
(.OSSS)
t.1644)
.1060
t.0429)
(.0424)
.0489 (.OSSS)
.1190 (.OS95)
.0073 c.0662) -.0647
(.0881)
-.1388
-.2086 (.06lS)
t.0432) Finance
(.0641)
.0199 t.7538)
-. 1054 t.0430)
-. 1640 l.0598)
.2488 t.0469)
-.0670 (.0971)
Clerical
.1498 t.0441:
-.1731 (.0983)
Craftsmen
.088S t.0427)
.06S2 (.ob90)
Operative8
.047s (.OS29)
.0382 ( .0669)
%NiCC
-.0266 t.04771
.OSb3
Service
Occupation
Variables
Professionals/ Emagero
1237
I
ii2 ik l *
636 .S280
significant sfgnlficant
at the 10 percent level, the 5 percent level,
at
-.27OO (.2160) 582 .s393 tuo-tailed
two-tailed
.ssss tests. team.
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170
Table
2.
Means
of
Variables Total ‘N =
Ln
Earnings
(Standard Sample 1237
XV, No. 1 & 2
Deviation
in
Blue Collar N - 636
JBI
parentheses) White N -
Collar 582
1.05
(0.55)
0.98
(0.51)
1.16
(0.55)
12.18
(2.79)
10.86
(2.23)
13.71
(2.46)
20.83
(14.98)
22.83
(15.54)
18.44
(13.79)
0.35
(0.48)
0.46
(0.49)
0.23
(0.42)
Female
0.38
(0.49)
0.38
(0.45)
0.51
(0.50)
Black
0.11
(0.31)
0.13
(0.34)
0.08
(0.27)
SMSA
0.34
(0.47)
0.30
(0.46)
0.40
(0.49)
South
0.24
(0.42)
0.26
(0.43)
0.20
(0.40)
0.35
(0.48)
0.37
(0.48)
0.33
(0.47)
Freedom
0.45
(0.49)
0.39
(0.48)
0.51
(0.50)
Skill
0.38
(0.48)
0.34
(0.47)
0.42
(0.50)
0.36
(0.47)
0.32
(0.47)
0.39
(0.49)
0.27
(0.44)
0.37
(0.48)
0.15
(0.36)
Schooling
(years)
Experience Union
Job
Membership
Characteristics:
Fast
Rard Physical
Work
Work
SPRING/SUMMER
and by lack of job experiences, and therefore mobility from one occupation or industry to another tends to be limited, at least in the short run. Thus to the extent that workers are ensconced within one occupation and industry mix, they will bargain for an optimal wage in view of the nonpecuniary aspects of the job.
IV.
conc1usiaIs
Job characteristics do have a signfficant effect on earnings. Indeed, qualitative variables which encompass work pace and physical exertion, and other variables which comprise responsibility and manual skill, contribute significantly in explaining variations in earnings even after controlling for other workers attributes. It is further shown that such job characteristics variables do not have the same effect on wages when we investigate separately the blue collar subgroup, work pace may be measured by the proxy variable FAST WORK to reflect the intensive utilization of capital by firms, while in the white collar subgroups it is accounted by the proxy FREEDOM to reflect the more flexible work schedule. While hard work is a disutility for a white collar worker who must have a higher pecuniary return to induce him to provide physical effort which he considers menial; hard and physical jobs have no statistical effect on blue collar earnings, to which only manual skill contributes significantly. In addition to the differential effects of job characteristics, the dichotomy between blue and white collar workers is also apparent in Personal attributes. The latter group has more schooling and job experience
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which induce positive and significant effects on earnings, while the negative effects of being female or residing in southern states are much smaller.
This paper does not attempt to answer the questionof "alienation"which,accordto industrial psychologists involves physical working conditions inducing dissatisfaction with work. Rather, our results suggest that within two broad occupational classifications, blue collar and white collar, certain aspects of the job entail higher pecuniary rewards than others. Accordingly, given that they have similar characteristics, workers' decisions about alternative occupations based on working conditions may explain variations in the distribution of employment among occupations and industries.
There are many ways by which the above One is to estimates could be improved. formulate a first difference specificacation or to incorporate latent variables, which could minimize, but could the biasing never completely remove, effects of omitted variables and measurement errors. The other, which is more important than some econometric but temporary cures, is to improve the "validity of the working condition measures, perhaps by gathering information about them at the workplace itself . .." [Duncan and Holmlund, 1982, p. 3751. However, this will lead us to rather detailed case and occupation, studies of industry, especially of worker behavior in the In any event, continued workplace. sophisticated use of biased data is not the answer.
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NOTES lNguyen T. Quan is with the Dept of Economics at Case Western Reserve University, Cleveland, OH 44106. The author is indebted to B. Allen and J. Beck for their helpful helpful comments. 2See Brown concise sumnary
[ 19801, Table 1, for a of studies on this topic.
3The model would not be complete without the firm's profit function. The marginal contribution of any worker to a firm's revenue product is likely to depend on a good match between abilities and job tasks, and on the occupational activities of all other workers. Hence, the net profit function generated by workers includes job characteristics. Maximization of such a profit function leads *to tangency points between the efficiency frontier, the isoprofit curves, and the conmton wage rate line. 4Empirical studies of the effects of human capital investment on earnings (see Parsley, 1980) posit a semi-log form of the earnings equation. Therefore the estimated equation has the following general form: h
Ek = b,
+ bl EdUCatiOnk
b2EXperienCek bjDeIaographiCjk bhDCCupatiOnhk b4Dnionk
+
+ bSExperiencek
+
+ blIndustrylk + b,,,Job AttribUteS,k
+ +
+ ek
where Ek is the average hourly received by the kth full-time The education variable is the
earnings worker. nu&er of
XV, No. 1 Q 2
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years of schooling the individual has completed, while the experience variable is the individual's age minus the years of schooling minus the first six years of his life. 5See the study by Herzberg, et al [1957] and the Report by the Committee on Work in Industry of the National Research Council [1941]. 6The dearth of survey data that encompass all needed variables in the same set such as workers' levels of earnings, workers personal characteristics, job descriptions, union membership and other exogeneous factors which could influence the level of wages, has forced us to make use of the Survey of Working Conditions. (Survey Research Center, Institute for Social Research, The University of Michigan, November 1969 - January 1970.) The survey covers 1533 workers, each with 660 variables of information regarding their actual job situations and areas affected by the job. This study selects a sample of 1237 individuals 16 to 65 years old and who are fully employed. The blue collar and white collar subgroups are selected according to the Dictionary of Occupational Titles (DOT) job Variables for classification. attempt to depict characteristics answers to the following questions: that FAST WORK: "A job that requires you work very fast" (How much is this like your main job?). "A job that allows you a FREEDOM: lot of freedom as to how you do your work." "A job that requires a high SKILL: level of manual skill." HARD WORK: *A job that requires that you work very hard." PHYSICAL: *A job that requires that
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you The the main
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exert a lot of physical effort." variables take the value of one if respondent answers "a lot like my job," and zero otherwise.
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for the estimate creases to .1247 old age subgroup.
for HARD WORK which inor 13.3% in the 45 years l ***
REFERENCES
7The marginal contribution of a regressor to the explanation of the dependent variable y, given all other regressors, is given by the partial correlation coefficient. The partial correlation coefficient between y and a regressor xj (after including all the other x's) is defined as
1. Bluestone, Barry. "The Personal Earnings Distribution: Individual and Institutional Determinants," Ph.D. Thesis, University of Michigan, 1974.
ryxj
2. Brown, Charles. "Equalizing Differences in the Labor Market," Quarterly Journal of Economics, Vol. 95, February 1980, pp. 113-134.
= (bj/Sbj)2/c(bj/Sbj)2
+ degree
of
freedom] where sbj is the standard error of the estimate bj. It is to be emphasized that the sum of the partial correlation coefficients is not in general equal to the For a R2 of the original regression. detailed discussion, see Naddala (1977, p. 110). 8The determination of percentage differential is obtained from (expbj - 1) where bj is the estimated coefficient Of xj. Also see Parsley [1980] for an extensive survey of union effects on wages. Prhe contribution of these variables to the explanation of the variations in earnings is 0.6X, 1.0%. 0.22, respectively, given all other regressors. loA disaggregation of the overall samPle into three different age groups. e.g. 35 Years old and above, 40 years old and above, and 45 years old and above, has been conducted. There is no significant change in the coefficient estimates of the job characteristic variables, except
3. Brown. k,
P. Henry. University
The Inequality of of California Press
1977. P. Sargent Florence, 8. 4. Davison, J.P., Gray, and N.S. Ross. Productivity and Economic Incentives, London: George Allen and Unwin Ltd., 1958. 5. Duncan, Greg and 8ertil Holmlund. "Was Adam Smith Right After All? Another Test of the Theory of Compensating Wage Differentials," Journal of Labor Economics, Vol. 1. 1983. pp. 368-379. 6. Duncan, Greg, and Frank Stafford. "Do Union Members Receive Compensating Wage Differentials?" American Economic Review, Vol. 70, June 1980, pp. 355-371. 7. Hamermesh, Daniel S. "Economic Aspects of Job Satisfaction," in Orley C. Ashenfelter and Wallace E. Oates (eds.) Essays in Labor Market Analysis, New York: John Wiley & Sons,
1977.
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R. 0. Peter8. Herzberg, F., B. Mansner, son, and D.F. Capwell. Job Attitudes: Review of Research and OpinPsychological Service of ion. Pittsburgh, 1957. 9.
10.
LaFitte, Paul. Social Personality in the York: MacMillan Co.,
G.S., McGraw Hill,
Econometrics, Inc., 1977.
Parsley, C.J. *Labor Unions and Wages: A Survey," Journal of Economic Literature, Vol. 58, March 1980, pp. 1-31.
14.
Quinn, Joseph. "The Early Retirement," M. I.T., 1975.
15.
Rosen, Sherwin. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, Vol. 79, January/February 1974, pp. 34-55.
16.
Smith, R.S. "Compensating Wage Differ entials and Public Policy, A Review,* Industrial Labor Relations Review, Vol. 32, April 1979, pp. 339-352.
17.
"Wages and Working ConSaffer, Henri. Working Paper No. 1418, ditions," National Bureau of Economic Research, August 1984.
and New
New York:
National Research Council, Committee on Work in Industry. Fatigue of , Workers: Its Relation to Industrial Production, Reinhold Publishing Corporation, N.Y., 1941.
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13.
Lucas, Robert E.B. "Hedonic Wage Equation and Psychic Wages in the ReSchooling," turn to American Economic Review, Vol. 67, September 1977. pp. 549-558.
11. Haddala,
12.
Structure Factory, 1958.
XV, No. 1 & 2
Microeconomics of Ph.D. Thesis,
COLLAR
T. Quad
Within the past decade, there have been numerous studies that attempt to estimate "prices" for nonwage task characteristics and to test the theory of equathat individuals lizing differences: are willing to accept less pleasant jobs provided that they receive compensating The differences in their wage rates. majority of those studies concentrate primarily on the effects of undesirable environmental working conditions, such as noise, weather, hazardous equipment, etc. . . . on wage rates, and very few of them have looked into the job itself, specifically on how the task is performed.2 Differences in wages among individuals are not only due to pecuniary and cognitive attributes but also to the characteristics of the task performed by the individual in the course of his or her job. However, the question of occupational attitude and work interaction between blue collar and white collar workers within a rigid hierarchical organization, and their expectations about task performance, has never been raised by researchers except by students of organizational behavior.
and other worker characteristics, physical aspects of the job do support the concept of equalizing differences and their effects differ for blue and white collar workers. The plan of the paper is as follows: Section I provides a simple model of blue collar and white collar workers behavior along with a short review of the empirical literature; Section II investigates the types of physical working conditions that may influence the wage structure in general and those of the blue-collar and white-collar occupations in particular; Section III presents a discussion of the empirical results followed by the conclusion.
I.
The Literature
and a Model
The theoretical literature on the hedonic aspect of prices for nonwage characteristics coalesced with the work of Rosen [1974] and R.E.B. Lucas [1977]. The theory was testedbyLucas [1977] with an hedonic wage equation: given the level of schooling, Lucas incorporates in the wage equation proxies for nonsedentary, repetitive and supervisory asphysical, pects of jobs. His results show that jobs which require lifting and physical exertion are not rewarded. In contrast, repetitive jobs and physical conditions of the workplace significantly explained small percentagesofthe increase in wage. Bluestone [I9741 and Quinn [1975] also investigate, via two different micro data sets, the effects of such physical characteristics of jobs on hourly wage. The
This paper will attempt to fill the void, first by proposing a model of worker behavior within the context of firm organization, and second, by investigating some of the tasks performed by blue and white collar workers and specifically their distaste toward certain reflected by job characteristics as The principal variation in earnings. finding of this paper is that, while controlling for personal, demographic
157
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authors provide contradictory results. In the former study, a job which requires physical strength has a significant but negative effect on wage, while in the latter the reverse is true. In both studies, bad working conditions, i.e., the environmental aspect of the workplace, have no significant effects. Duncan and Stafford [1980] have extended the basic model of compensating differential to include the role of unions on the pace of work. They conclude that unionized blue-collar workers enjoy large wage increases but at the expense of declining desirable working conditions. However, they fail to obtain significant compensating differentials for white-collar workers. Smith [1979] comments in a recent survey, that failure to provide convincing evidence of compensating wage differentials is due to problems of comparability across individuals in self-reported data and to problems of describing disagreeable working conditions due to heterogeneous tastes. Further, Hamermesh [I9771 argues that a worker perception of badness in the work place tends to improve with rising and specific training and, in wage general, high wage occupations have better working place and contented workers. Hence there is a strong degree of simultaneous bias in the estimation working conditions of self-reported measures and level of earnings. Brown [1980] in an effort to minimize the problem of omitted variables and measurement error that could have engendered bias in the estimated coefficients, uses a first difference specification so that ability, which is measured at two points in time, is unrelated to both the change in earnings and the change in working conditions. He showed no empirical evidence of compensating
XV, No. 1 & 2
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difference. In contrast, Duncan and Holmlund [1983], and Saffer [1984] provide empirical results that support the compensating difference hypothesis. In the former study, two points in time changes in self-reported job characteristics are used as regressors for changes in relative wages, while in the latter study a single comprehensive working condition variable is used via a system of simultaneous equations and generalized probit technique. However, Saffer shows that the first difference specification will not eliminate the bias inherent in self-reported data. This is one of the reasons that the Duncan and Holmlund results show many estimated coefficients with the "wrong" signs. Although Saffer's sophisticated approach may provide efficient estimates, the construction of a single unobserved latent variable as a proxy for working conditions does not enlighten the reader as to the complexities of the job tasks. Model Consider a firm that employs n1 number of blue-collar workers or production and ng number of white-collar workers, workers or nonproduction workers. Each group of workers performs a well defined but different set of tasks in their job assignment. Moreover, one can presume that, in the context of firm operations, white collar workers actively promote their expertise onto the blue collar workers. For example, the white collar group could decide on various production techniques or on the purchase of capital equipment that would constrain the blue collar group to have specific workerSecond, white machine arrangements. collar workers are loathe to carry Out tasks usually assigned to blue collar
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Sitting at a desk and writing workers. directives is preferred to assembling machinery components at a specific speed. Third, white collar groups will attempt to maintain their preferred status quo in the organization. Workers'
Choices
We posit that task performance is an argument of the utility function, and that "dissatisfaction (results) from performing a task that involves the use of of an ability than is more, or less, possessed by the person" [Lucas, 1977, p. 5503. Thus we can presume that a person with managerial ability but of low strength may enjoy being an administrator, and the same person may be repulsed by the prospect of fast paced mechanical work (at the same wage rate). We also assume that wages vary across jobs and workers. Further, because of innate ability and work assignment rigidity, not every job is included in the worker's there will be a choices set. However, wage rate compatible with the excluded jobs. Jobs within the white collar Occupations are excluded from the blue collar choice set. Here we assume that blue collar workers are ensconced in their occupational jobs and, at least in the short run, mobility toward a white collar assignment is restricted, Since explicit and implicit costs of training are prohibitive. Conversely, white collar workers will not yearn for blue collar jobs. If, for one reason or another, a white collar worker has to Perform a blue collar task to which he/she is averse, then he/she demands compensation.
both
We also about
postulate that the pecuniary
workers rewards
care from
1986
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his work and about the "quality" of his/her job. Quality may be described by some vector of attributes of the job performed, denoted herein as vector Zi for job i. Attitudes to pecuniary and non-pecuniary rewards vary from worker to worker, however, it is safe to assume that tastes are partially conditioned by some vector C of measured personal characteristics of the worker. Within the blue collar worker choice set, we have the following worker's utility function with hedonic factors.
Uk = ui(wbf, where
WEi is the kth worker
wwi 9 zbi s Ck) blue collar on the ith
W,,i is the white on the ith job Zbi tics
(1)
wage for job
collar
the
wage paid
is the Vector of characterisfor the ith blue collar job
Ck is the acteristics On the other worker's utility as
vector of personal for worker k
char-
hand, the white collar function can be written
Ui = Uw(&s wbi, ZwisCk)
(2)
where the w subscript denotes white collar worker and Wbi is the blue collar wage paid for the ith job. It should be emphasized that Wwi and Wbi are wages that are based on personal characteristics such as skill and tastes, and are not influenced by job attributes. The above discussion brings forth the followa) in equation ing formal assumptions: (l), both Wii and white collar wage Wwi
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have a positive effect on Ub. Even though the hourly wage rate found in some white collar jobs is lower than most blue collar jobs, workers in blue collar occupations tend to regard white collar occupations to be "better" than their own. That is, white collar jobs induce a "positive within the firm's taste." For example, rigid organizational framework, an engineer has more privileges than a technician,eventhoughthefonner's hourly wage maybeless than the latter's. Conversely, in equation (2) unless the blue collar hourly wage is higher than the white collar wage to offset any disutility associated with blue collar job attributes, Ubi could have no effect, or even a negative effect, on the white collar preference function; b) although the total effects of Zwi, the vector of white collar job characteristics. on U, is unknown (obviously an unpleasant task should be compensated), the effect of a distasteful blue collarjob'sattributes zbi will be stronger on Uw than on Ub. That is, white collar workers are more averse to blue'collar job attributes than blue collar workers to their own jobs. To induce white collar workers to hold jobs that have blue collar attributes, the former must be compensated accordingly; c) the level of compensation depends on the blue and white collar wage difMore specifically, the wage ferential. earned by a blue collar worker on job i should be greater than the one earned by a white collar worker on that same job. If both wage levels were the same, and given the aversion of white collar workers to unpleasant conditions, then in order to keep the latter on the same indifference curve, they will have to hold blue collar jobs that have a lower degree of unpleasantness; d) the status quo aspired by white collars will be
XV, No. 1 & 2
modeled herein as a predetermined level $. A blue collar worker will attempt to optimize his/her utility subject to the white collar constraint U2, e) the measurable personal characteristic variable Ck that could be constructed as a distinguishing feature between blue and white collar workers is the years of schooling. Graphically the model assumes that workers choose from a set of jobs bounded by a frontier such as FlFl illustrated in Figure 1. Given a cormK)n wage offered to a group of workers who have a distaste for a certain job characteristic, whether they are blue or white collars, and given complete information and various tastes, efficient job choosers distribute them selves along FlFl at points where their indifference curves are tangential to the frontier. Each coaaaon wage offer has a positive slope given a universal distaste for that job characteristic. The intercept b/b of the wage line measures both the pecuniary and the nonpecuniary aspects of wage for blue collar worker k with indifference curve Ub. Ub is affected only by personal tastes and not bY Similarly some job characteristics. Figure 1 can also be used to depict white collar workers' behavior by relabelling the axes to white collar wage and white However, if collar job characteristics. a white collar worker were to take a job with blue collar attributes, his aversion to the job will make the rate of substitution between his wage and that distasteful blue collar job characteristic higher than the substitution between the blue collar wage and its own job characteristic. In terms of a graph relating wages and blue collar job characteristics, a white collar worker's indifference curve has a stronger curvature than
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SPRING/SUMMER
FIGURE
blue
1986
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1
Wage. 4 collar
‘b
. Blue collar job characteristic
162
VOLUMEXV,No.l&
FIGURE
2
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2
Wage
"b
.
.
job
Blue collar characteristic
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SPRING/SUMMER
the one for blue collar worker. This is shown in Figure 2 where the vertical axis is the wage level for both groups of workers, and the horizontal axis is the blue collar job characteristics. To maintain the white collar indifference curve at a higher level than the blue collar, and given that white collar workers have a stronger distaste for a blue collar job attribute, the slope of the wage line tangent to the white steeper collar indifference curve is than the one for blue collars. The resulting equilibrium points between white and blue collar indifference curves and their respective wage lines at a given blue collar job characteristic are shown in Figure 2 as D and A, respectively. It should be noted that points D, A, and z are lined up on the same hyperplane. The slopes of W,D and WbA represent the job attribute affected wages for white and blue collar workers, respectively. To induce a white collar worker to hold a job with blue collar attribute, the compensation constraint dictates that the taste affected W, be smaller than Wb. These are represented by the interOtherwise, as cepts on the wage axis. illustrated in Figure 3, both white and blue collar worker equilibrium points with their respective indifference curves will not be lined up on the same hyperplane. The tangency point between U, and the wage line is at F rather than at D. In this case an uncompensated white collar worker is willing to perform a blue collar task only if the degree of disamenity is less. It should be pointed Out that for white collar workers whose jobs are far removed from the plant or the shop floor, then both blue and white collar utility sets are mutually exclusive, comparative analysis as hence suggested herein will be difficult.
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Figure 2 also provides an extension to the above analysis. If the white collar worker were to be paid the same job attribute affected wage as the blue collar worker, then the former, in order to maintain the same level of utility, will substitute for more pleasant jobs such as at point E. In this case the wage line Wb' is parallel to Wb. The same can be said for point G in Figure 3. Workers maximize their job choices by equating the marginal rates of substitution between wage rate and each job characteristic with the ratio of their shadow prices at the relevant point on the efficiency frontier F~FI.~ The resulting optimization provides the following set of hedonic wage equations
II.
wbki = w(zbis
&is
Ck)
(3)
w:f = w(zbi.
&is
Ck)
(4)
Vwlrbles
and Data
The theory on hedonic wage equations does not provide any guidance in the choice of functional forms. A convenient specification for the estimation of wage differentials is a regression of the natural logarithm of average hourly earnings on a set.of dunvny variables representing population characteristics, human capital indicators, industrial and occupational characteristics.4 Job Characteristics The problems of working environment and task performance have been primarily scrutinized by industrial psychologists.5 For example, LaFitte Cl9581 has observed that some skilled workers perform well and are satisfied with their jobs while
164
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XV, No. 1 i?i 2
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FIGURE 3
'b
* Blue collar job characteristic
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SPRING/SUMMER
others do the minimum required by their employers and report themselves as dissatisfied with their jobs. These researchers also point out that this dichotomy is significantly associated with the observed dichotomy among tasks between clean and light, or between heavy and dirty. In other words, workers regard themselves as well off and are highly cooperative and highly productive when they appreciate the superior physical conditions of their occupation. This positive relationship between worker satisfaction and working conditions may be translated into fluctuations in earnings due to bonus payment or discretionary pecuniary awards, for example for good time keeping or saving of materials. However, because incentive payment may have to be provided in order to keep qualified workers satisfied and productive, it is difficult to render generalfzations on the effectiveness of economic incentives without looking at the task itself. As discussed by Davison, et al response to incentive [1958], workers' payment will vary according to the degree of skill and concentration involved. Of considerable significance is whether the output is determined by machine or determined by the operator himself, and whether the work is heavy or light, varied or repetitive. Furthermore, the relationship of the operation to previous and succeeding operations is also significant since it requires the worker to have some physical coordination and mental concentration in the production of the output. Hence Davison states that "variations, actual or anticipated, in the flow of work may exert considerable influence on workers' reactions to incentive payment schemes" [Davison, et al., 1958, p. gg]. In this case, fluctuations in earnings brought about by piece rates
1986
165
or other form of payment by results, depend on worker skill and stamina. Moreover, within the same occupation differences in personal aptitude are often associated with wide differences in the tasks performed. Brown [lg77] points out that the variance of earnings for professional and technical occupations arises from differences in the level of responsibility and also from personal differences at each level: that is, average earnings increase with the level of responsibility but so does the range As job qualification reof earnings. quirements increase, differences in personal ability make more difference to the product. This would provide a higher pecuniary reward than for jobs that can be performed by less qualified persons. In sum, the intrinsic aspects of the job shape workers' attitudes, prompting them to ask for equalizing differences in pecuniary reward. The Data The data and variables come from the Survey of Working Conditions.C Unique to this sample is a set of variables which encompasses workers' descriptions of their working environment and jobs. Out of the set we have chosen five variables which provide a description of the worker's perception of his/her job. From the discussion presented above, we have looked specifically at the pace of the work itself, the physical aspect of the work and the responsibility concomitant The self-reported job desto the job. cription is incorporated into a binary For example, the variable FAST variable. WORK describing a job that requires an individual to work very fast, is assigned a value one if the worker answers in the affirmative, and zero otherwise.Similar-
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ly, binary explanatory variables are also used for jobs which require hard work (HARD WORK) and physical effort (PHYSIand for jobs which allow some CAL), degree of freedom (FREEDOM). The variable FREEDOM is used to describe "a job that allows you a lot of freedom as to how you do your work. In other words, FREEDOM is not meant to describe idleness or free time on the job, but it is a proxy for a job which requires some degree of responsibility from the holder. The variable PHYSICAL is a proxy for manual task where the use of a machine is minimal or nonexistent. Moreover, to take into account the prior proposition that, besides the intellectual skill provided by formal schooling, physical skill may cause, at least in the short run, large wage differences, another binary variable, SKILL, is included in the earnings equation for jobs which are described as demanding some high degree of manual skill.
III.
Findings
The least squares method is applied to a sample of all workers, and separately to the blue collar and white collar subResults are shown in Table 1. groups. In column 1, estimated coefficients for the relevant variables in the overall sample are presented. Columns 2 and 3 tabulate regression results for the blue collar and white collar worker samples, Estimates are classified respectively. such as working condiby categories, tions, industries, occupations. The value of the adjusted R2 is relatively high given the nature of the data set. For 1237 observations, the adjusted R2 is about -53, this is due in part to the inclusion of industry and occupa-
XV, No. 1 & 2
tional variables. As expected, the estimated coefficients for human capital indicators and individual demographic characteristics have the theoretically correct sign and are highly significant. For example, in the all workers sample, an extra year of experience will raise hourly earnings by 1.7 percent, while an extra year of schooling will increase earnings by 6.1 percent. As discussed above, one way to distinguish white collar from blue collar personal attributes istheformer's affinityforschooling. The schooling incremental effects on blue collar and white collar earnings are 4.9 percent and 7.0 percent, respectively. Of greater interest is the marginal contribution of each regression variable in the explanation of the variation in earnings. Thus the experience variable contributes, with significant diminishing returns, about 5.1 percent to the explanation of the variations in earnings given all other regressors.7 This contribution drops to 4.7 percent in the blue collar sample, but increases drastically, up to 88.4 percent in the white collar workers sample. At the same time the education variable contributes about 8.7 percent to the explanation in earnings for the overall sample, while it is 5.8 percent for the blue collar sample and 10.7 percent for the white collar sample. This result reinforces the notion that formal schooling and post-schooling experiences, in contrast to blue collar occupations, are strong elements in explaining variations in earnings within the white-collar occupation. The dichotomy between white and blue collar groups is also apparent among female, unionized, For instance, female and urban workers. workers earn on the average 36 percent less than their male counterparts, with a greater discrepancy in the blue-collar
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sample. As far as residential locations and regions are concerned, workers living in large urban areas have a 17.5 percent edge and southerners earn about 15 percent less than workers in other parts of the country. However, white collar workers in urban areas have higher earnings than any other group. At the same time, they do not suffer financially as much for residing in the South. The estimated relative effect of in the overall unionism on earnings sample is .1239 or a non-union earnings differential of 13.2 percent. The difference in the effects of unionism on earnings is clearly significant when the sample is disaggregated into blue-collar and white-collar. The proportionate differential is about 23.7 percent in the blue-collar category, while it is almost nil in the white-collar.* Variables depicting job characteristics encompass mainly the physical and supervisory sides of a job. The estimated coefficients for variables in the overall sample such as freedom on the job (FREEDOM), job requiring manual skill (SKILL), and job requiring hard work (HARD WORK) are all statistically significant at least at the 10 percent level and have the expected sign.g Freedom on the job provides the worker with some degree of responsibility which implies either intellectual ormanual ability. One expected result is the highly significant, but coefficient for negative, estimated the variable depicting physical effort on the job (PHYSICAL). One reason is that jobs in which physical efforts are needed are generally menial jobs and for which machine utilization is minimal. In contrast the estimated coefficient for the variable FAST WORK is not significant.lO
1986
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The regressions for blue collar and white collar subgroups provide some interesting contrasts, as seen in columns 2 and 3. For the blue collar workers, the coefficient for FAST WORK becomes significant at the 10 percent level, as does SKILL; while the estimate for PHYSICAL is statistically significant at the 20 percent level only. FREEDOM and HARD WORK are not at all significant. In the white collar subgroup, FAST WORK has very little effect on earnings as expected from that group of workers, but coefficients for FREEDOM, HARD WORK, and PHYSICAL do show a significant explained variation in earnings. The first two variables have positive estimated coefficients while the latter is negative. In other words, work pace is best measured by the proxy variable FAST WORK for the blue collar worker to reflect the intensive utilization of capital employers, while it is best accounted for by the proxy FREEDOM in the white collar group to reflectthemore flexible work schedule and the higher degree of responsibility. Moreover, hard work is a disutility for a white collar worker, so that he must be compensated proportionately. Having to provide physical effort is considered In contrast, for a blue collar menial. worker, hard and physical jobs are considered the norms, so that these proxies do not provide any statistical effect on earnings, while acquiring a skill should reflect opportunity costs and obstacles to entry. The inclusion of occupational and industrial variables in the regression may have taken away some of the explanatory power that legitimately belongs to the working conditions variables. However it is safe to state that individual workers in general are restricted in their job search by inadeor sometimes incorrect training, quate,
16::
VOLUME
Table 1. Regression estimates standard error in parentheses).
Explanatory Variables
All Workers (1)
XV, No. 1 & 2
of In Hourly
Earnlngs.(absoltite
Blue Collar
(2)
value
of
White Collar (3)
Constant
.1052 (.0877)
Union
.1239** (.0249)
Exper
.0170** (.0021)
.0149** (.0027)
.0196** (.0003)
Exper2
-.00027** (.00004)
-.00022** (.00005)
-.00032** (.00007)
Education
.0593** (.OOSS)
.0487** C.0079)
.0672** (.0082)
Female
-.4399** c.0267)
-.5020** (.0379)
-.4277** (.0356)
Black
.0191 t.0361)
.0285 c.0446)
.0571 (.0606)
SnsA
.1610** c.0251)
.1065** C.0344)
.1847** C.0353)
South
-. 1393** (.0290)
-.1584** c.0385)
-.1051** (.0436)
Fast Work
.0350 t.0263)
.0590* c.0358)
.0144 (.0378)
Preedom
.0601** (.0228)
.0328 (.0307)
.0804** C.0334)
Skill
.0894** (.0252)
.1175** (.0325)
.0431 (.0385)
Hard Work
.0479* (.0274)
-.00297 (.0390)
.0911** (.0376)
Physical
-.0820** (.0282)
-.0525 C.0345)
-.0921* c.04771
.1656 (.1158)
.3560 (.1611)
.2129 (0.450)
-.0174 (.0400)
Job Characteristics:
SPRING/SUMMER
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Industries
1986
169
Variables
Construction
.1032 (.0600)
Manufacture
.ObO?
Transport Wholesale/Retail
.1103
.1474
(.OSSS)
t.1644)
.1060
t.0429)
(.0424)
.0489 (.OSSS)
.1190 (.OS95)
.0073 c.0662) -.0647
(.0881)
-.1388
-.2086 (.06lS)
t.0432) Finance
(.0641)
.0199 t.7538)
-. 1054 t.0430)
-. 1640 l.0598)
.2488 t.0469)
-.0670 (.0971)
Clerical
.1498 t.0441:
-.1731 (.0983)
Craftsmen
.088S t.0427)
.06S2 (.ob90)
Operative8
.047s (.OS29)
.0382 ( .0669)
%NiCC
-.0266 t.04771
.OSb3
Service
Occupation
Variables
Professionals/ Emagero
1237
I
ii2 ik l *
636 .S280
significant sfgnlficant
at the 10 percent level, the 5 percent level,
at
-.27OO (.2160) 582 .s393 tuo-tailed
two-tailed
.ssss tests. team.
VOLUME
170
Table
2.
Means
of
Variables Total ‘N =
Ln
Earnings
(Standard Sample 1237
XV, No. 1 & 2
Deviation
in
Blue Collar N - 636
JBI
parentheses) White N -
Collar 582
1.05
(0.55)
0.98
(0.51)
1.16
(0.55)
12.18
(2.79)
10.86
(2.23)
13.71
(2.46)
20.83
(14.98)
22.83
(15.54)
18.44
(13.79)
0.35
(0.48)
0.46
(0.49)
0.23
(0.42)
Female
0.38
(0.49)
0.38
(0.45)
0.51
(0.50)
Black
0.11
(0.31)
0.13
(0.34)
0.08
(0.27)
SMSA
0.34
(0.47)
0.30
(0.46)
0.40
(0.49)
South
0.24
(0.42)
0.26
(0.43)
0.20
(0.40)
0.35
(0.48)
0.37
(0.48)
0.33
(0.47)
Freedom
0.45
(0.49)
0.39
(0.48)
0.51
(0.50)
Skill
0.38
(0.48)
0.34
(0.47)
0.42
(0.50)
0.36
(0.47)
0.32
(0.47)
0.39
(0.49)
0.27
(0.44)
0.37
(0.48)
0.15
(0.36)
Schooling
(years)
Experience Union
Job
Membership
Characteristics:
Fast
Rard Physical
Work
Work
SPRING/SUMMER
and by lack of job experiences, and therefore mobility from one occupation or industry to another tends to be limited, at least in the short run. Thus to the extent that workers are ensconced within one occupation and industry mix, they will bargain for an optimal wage in view of the nonpecuniary aspects of the job.
IV.
conc1usiaIs
Job characteristics do have a signfficant effect on earnings. Indeed, qualitative variables which encompass work pace and physical exertion, and other variables which comprise responsibility and manual skill, contribute significantly in explaining variations in earnings even after controlling for other workers attributes. It is further shown that such job characteristics variables do not have the same effect on wages when we investigate separately the blue collar subgroup, work pace may be measured by the proxy variable FAST WORK to reflect the intensive utilization of capital by firms, while in the white collar subgroups it is accounted by the proxy FREEDOM to reflect the more flexible work schedule. While hard work is a disutility for a white collar worker who must have a higher pecuniary return to induce him to provide physical effort which he considers menial; hard and physical jobs have no statistical effect on blue collar earnings, to which only manual skill contributes significantly. In addition to the differential effects of job characteristics, the dichotomy between blue and white collar workers is also apparent in Personal attributes. The latter group has more schooling and job experience
1986
171
which induce positive and significant effects on earnings, while the negative effects of being female or residing in southern states are much smaller.
This paper does not attempt to answer the questionof "alienation"which,accordto industrial psychologists involves physical working conditions inducing dissatisfaction with work. Rather, our results suggest that within two broad occupational classifications, blue collar and white collar, certain aspects of the job entail higher pecuniary rewards than others. Accordingly, given that they have similar characteristics, workers' decisions about alternative occupations based on working conditions may explain variations in the distribution of employment among occupations and industries.
There are many ways by which the above One is to estimates could be improved. formulate a first difference specificacation or to incorporate latent variables, which could minimize, but could the biasing never completely remove, effects of omitted variables and measurement errors. The other, which is more important than some econometric but temporary cures, is to improve the "validity of the working condition measures, perhaps by gathering information about them at the workplace itself . .." [Duncan and Holmlund, 1982, p. 3751. However, this will lead us to rather detailed case and occupation, studies of industry, especially of worker behavior in the In any event, continued workplace. sophisticated use of biased data is not the answer.
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NOTES lNguyen T. Quan is with the Dept of Economics at Case Western Reserve University, Cleveland, OH 44106. The author is indebted to B. Allen and J. Beck for their helpful helpful comments. 2See Brown concise sumnary
[ 19801, Table 1, for a of studies on this topic.
3The model would not be complete without the firm's profit function. The marginal contribution of any worker to a firm's revenue product is likely to depend on a good match between abilities and job tasks, and on the occupational activities of all other workers. Hence, the net profit function generated by workers includes job characteristics. Maximization of such a profit function leads *to tangency points between the efficiency frontier, the isoprofit curves, and the conmton wage rate line. 4Empirical studies of the effects of human capital investment on earnings (see Parsley, 1980) posit a semi-log form of the earnings equation. Therefore the estimated equation has the following general form: h
Ek = b,
+ bl EdUCatiOnk
b2EXperienCek bjDeIaographiCjk bhDCCupatiOnhk b4Dnionk
+
+ bSExperiencek
+
+ blIndustrylk + b,,,Job AttribUteS,k
+ +
+ ek
where Ek is the average hourly received by the kth full-time The education variable is the
earnings worker. nu&er of
XV, No. 1 Q 2
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years of schooling the individual has completed, while the experience variable is the individual's age minus the years of schooling minus the first six years of his life. 5See the study by Herzberg, et al [1957] and the Report by the Committee on Work in Industry of the National Research Council [1941]. 6The dearth of survey data that encompass all needed variables in the same set such as workers' levels of earnings, workers personal characteristics, job descriptions, union membership and other exogeneous factors which could influence the level of wages, has forced us to make use of the Survey of Working Conditions. (Survey Research Center, Institute for Social Research, The University of Michigan, November 1969 - January 1970.) The survey covers 1533 workers, each with 660 variables of information regarding their actual job situations and areas affected by the job. This study selects a sample of 1237 individuals 16 to 65 years old and who are fully employed. The blue collar and white collar subgroups are selected according to the Dictionary of Occupational Titles (DOT) job Variables for classification. attempt to depict characteristics answers to the following questions: that FAST WORK: "A job that requires you work very fast" (How much is this like your main job?). "A job that allows you a FREEDOM: lot of freedom as to how you do your work." "A job that requires a high SKILL: level of manual skill." HARD WORK: *A job that requires that you work very hard." PHYSICAL: *A job that requires that
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you The the main
SPRING/SUMMER
exert a lot of physical effort." variables take the value of one if respondent answers "a lot like my job," and zero otherwise.
1986
173
for the estimate creases to .1247 old age subgroup.
for HARD WORK which inor 13.3% in the 45 years l ***
REFERENCES
7The marginal contribution of a regressor to the explanation of the dependent variable y, given all other regressors, is given by the partial correlation coefficient. The partial correlation coefficient between y and a regressor xj (after including all the other x's) is defined as
1. Bluestone, Barry. "The Personal Earnings Distribution: Individual and Institutional Determinants," Ph.D. Thesis, University of Michigan, 1974.
ryxj
2. Brown, Charles. "Equalizing Differences in the Labor Market," Quarterly Journal of Economics, Vol. 95, February 1980, pp. 113-134.
= (bj/Sbj)2/c(bj/Sbj)2
+ degree
of
freedom] where sbj is the standard error of the estimate bj. It is to be emphasized that the sum of the partial correlation coefficients is not in general equal to the For a R2 of the original regression. detailed discussion, see Naddala (1977, p. 110). 8The determination of percentage differential is obtained from (expbj - 1) where bj is the estimated coefficient Of xj. Also see Parsley [1980] for an extensive survey of union effects on wages. Prhe contribution of these variables to the explanation of the variations in earnings is 0.6X, 1.0%. 0.22, respectively, given all other regressors. loA disaggregation of the overall samPle into three different age groups. e.g. 35 Years old and above, 40 years old and above, and 45 years old and above, has been conducted. There is no significant change in the coefficient estimates of the job characteristic variables, except
3. Brown. k,
P. Henry. University
The Inequality of of California Press
1977. P. Sargent Florence, 8. 4. Davison, J.P., Gray, and N.S. Ross. Productivity and Economic Incentives, London: George Allen and Unwin Ltd., 1958. 5. Duncan, Greg and 8ertil Holmlund. "Was Adam Smith Right After All? Another Test of the Theory of Compensating Wage Differentials," Journal of Labor Economics, Vol. 1. 1983. pp. 368-379. 6. Duncan, Greg, and Frank Stafford. "Do Union Members Receive Compensating Wage Differentials?" American Economic Review, Vol. 70, June 1980, pp. 355-371. 7. Hamermesh, Daniel S. "Economic Aspects of Job Satisfaction," in Orley C. Ashenfelter and Wallace E. Oates (eds.) Essays in Labor Market Analysis, New York: John Wiley & Sons,
1977.
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R. 0. Peter8. Herzberg, F., B. Mansner, son, and D.F. Capwell. Job Attitudes: Review of Research and OpinPsychological Service of ion. Pittsburgh, 1957. 9.
10.
LaFitte, Paul. Social Personality in the York: MacMillan Co.,
G.S., McGraw Hill,
Econometrics, Inc., 1977.
Parsley, C.J. *Labor Unions and Wages: A Survey," Journal of Economic Literature, Vol. 58, March 1980, pp. 1-31.
14.
Quinn, Joseph. "The Early Retirement," M. I.T., 1975.
15.
Rosen, Sherwin. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, Vol. 79, January/February 1974, pp. 34-55.
16.
Smith, R.S. "Compensating Wage Differ entials and Public Policy, A Review,* Industrial Labor Relations Review, Vol. 32, April 1979, pp. 339-352.
17.
"Wages and Working ConSaffer, Henri. Working Paper No. 1418, ditions," National Bureau of Economic Research, August 1984.
and New
New York:
National Research Council, Committee on Work in Industry. Fatigue of , Workers: Its Relation to Industrial Production, Reinhold Publishing Corporation, N.Y., 1941.
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13.
Lucas, Robert E.B. "Hedonic Wage Equation and Psychic Wages in the ReSchooling," turn to American Economic Review, Vol. 67, September 1977. pp. 549-558.
11. Haddala,
12.
Structure Factory, 1958.
XV, No. 1 & 2
Microeconomics of Ph.D. Thesis,