8 Compensation and Working Conditions Winter 1999

BROOKS PIERCE

Using the National

Compensation Survey to

Predict Wage Rates

The National Compensation Survey (NCS) combines

three former Bureau of Labor Statistics’ establishment

survey programs: The Employment Cost Index, the

Employee Benefits Survey, and the Occupational

Compensation Survey. As part of the NCS, the Bureau

collects data on job characteristics, dubbed “generic

leveling factors.” This study demonstrates the usefulness

of these data in predicting wage rates.

Brooks Pierce is an economist in the Compensation Research and Program Development

Group, Bureau of Labor Statistics. The results

presented here rely heavily on material produced

by Maury Gittleman, an economist in the same

office.

Telephone: (202) 691-7525

E-mail: Pierce_B@bls.gov

The National Compensation Survey (NCS) is designed to be anationally representative random sample of jobs in the non-agricultural, non-Federal economy. Field

economists visit sampled establishments and obtain information on the

establishment and a sample of jobs in

the establishment. At present the data

collected are mainly attributes of the

job and establishment, and earnings

and work schedules. Establishment

data include employment size and location, the establishment’s industry according to the Standard Industrial

Classification (SIC) system, and

whether the establishment is privately

owned or operated by a State or local

government. Occupation-specific data

include Bureau of the Census occupational classifications, whether the occupation is covered by a union contract, whether the job is part time or

full time, and generic leveling factors

data. Earnings data, including incentive pay data, are collected for the individuals in a sampled occupation; per

hour wage rates are calculated in conjunction with work schedule information for the occupation.

The generic leveling factors are

designed to measure job duties. There

are at present 10 factors, each with

various levels: Knowledge (9 levels),

supervisory controls (5), guidelines

(5), complexity (6), scope and effect

(6), personal contacts (4), purpose of

contacts (4), physical demands (3),

work environment (3), and supervisory

duties (5). The factor names largely

reflect their content. For example, part

of the description of the first level of

knowledge is “...simple, routine, or repetitive tasks or operations which typically include following step-by-step

instructions and requires little or no

previous training or experience.” In

comparison, the fourth level of knowledge is described in part as “Knowledge of an extensive body of rules, procedures, operations, products or

services requiring extended training
 Compensation and Working Conditions Winter 1999 9

Predicting Wage Rates

and experience to perform a wide variety of interrelated or nonstandard

procedural assignments and resolve a

wide range of problems.”

These data elements are “generic”

in the sense that they do not rely on

identifying the occupation in question.

This facilitates the collection of these

data for random samples of jobs that

cover the broad range of occupations

in the economy. It also gives some

basis for comparing or classifying occupations that are distinct but that may

have similar duties and responsibilities. Although designed to describe

the job and not the workers in the job,

these factors are in practice likely to

be related to job incumbent abilities, for

the simple reason that employers recruit

workers in such a way as to match employee abilities with job duties.

The sample used in this article corresponds to that used for the 1997 national NCS summary publication. The

sample includes 145,054 jobs from

17,246 different establishments.

Employment distribution by generic leveling factors

Table 1 shows employment distribution by generic leveling factors. These

distributions are silent on whether jobs

are properly classified and on whether

each level is clearly distinct from its

neighbors. Nevertheless, very sparse

data cells might suggest that too-fine

distinctions are being made, whereas

cells with excessive amounts of data

would suggest that the scale as constituted is not very useful in distinguishing one job from another.

For most of the factors, the scales

do not indicate such data problems.

The knowledge scale has the most levels, by design, as wages are known to

be closely related to factors this scale

captures, and because it is probably

easier to make meaningful distinctions

along this dimension. For this factor

and the next four, jobs are widely dispersed among the categories. The remaining five factors tend to have more

clumping of the data. This largely reflects the fact that many jobs simply

do not have duties associated with the

factor in question. For example, most

jobs are not supervisory in nature, so

the masses of the data are in the lower

levels of supervisory duties. Many jobs

are sedentary and have low physical

demands (the physical demands scale

is from less to more exertion), and

most work environments involve only

everyday risks (the work environment

scale is from low risk to high risk). It

is possible that collecting some of these

data in less detail would reduce respondent burden without unduly reducing

the factors’ predictive power for

wages. Whether or not the scale distinctions for, say, supervisory duties,

can help predict wages independently

of the other factors is an issue this article addresses.

Correlation among factors

One aspect of these data that is not

obvious from table 1 is the question of

how independent one factor’s information is from the others’. That is, does

knowing a job’s complexity level tell

you much more about the job than if

you had only known the job’s knowledge level? This is an important question because its answer gives some idea

about how redundant the information

incorporated in, for example, “complexity” is to that incorporated in

“knowledge.” Indeed, if position in

the complexity scale can be completely

and accurately determined from position in the knowledge scale, there is

no reason to construct the complexity

scale at all.

Table 2 gives rank order correlation coefficients between the various

factors to address this general question. The most striking result here is

the large positive correlation among

the first seven factors, which suggests

some duplication of data. Therefore,

there may be difficulty in disentangling the effects of knowledge on

wages from the effects of, say, complexity on wages. However, despite

the high correlation, each factor may

still contribute to explaining wages or

otherwise classifying jobs because each

factor may embody information not

contained in the other factors. The

supervisory duties variable is somewhat less correlated with knowledge.

The physical demands and work environment variables are positively correlated with each other, and are somewhat negatively correlated with

knowledge and the other factors.

Many of the factors seem related to

knowledge. As an example, table 3

compares distributions of complexity

levels conditional on different levels

of knowledge. This gives some indication of the interrelated nature of the

factors. Complexity is chosen only as

an example; the qualitative point is

similar for other factors. Over 95 percent of sampled jobs with knowledge

level 1 also have the lowest level of

complexity. Clearly, higher levels of

knowledge are associated with higher

levels of complexity. For instance, at

knowledge level 2 about 35 percent of

TABLE 1. Employment distribution by generic leveling factor across levels, 1997

National Compensation Survey

(Percent)

Knowledge .................. 100 13.1 28.0 18.7 11.8 6.6 13.9 6.3 1.5 0.1

Supervision received ... 100 25.6 40.1 27.0 6.3 1.0 - - - -

Guidelines .................. 100 37.7 34.1 22.6 4.8 .7 - - - -

Complexity .................. 100 22.6 35.1 33.1 6.3 2.8 .2 - - -

Scope and effect ......... 100 34.6 33.3 26.5 4.3 1.2 .1 - - -

Personal contacts ....... 100 48.1 40.0 11.6 .3 - - - --

Purpose of

 contacts ................... 100 64.6 26.7 8.1 .7 - - - --

Physical demands....... 100 40.7 56.6 2.8 - - - - --

Work environment ....... 100 49.2 48.9 1.9 - - - - --

Supervisory duties ...... 100 81.6 7.9 8.7 1.7 .2 - - - -

NOTE: Dash indicates data not applicable. Due to rounding, sums of percentages may not equal 100.

Generic leveling factor Total

Level

1234 5 6 7 9 8
10 Compensation and Working Conditions Winter 1999

the jobs have a complexity level of 1,

and most are classified in the next

higher level. The dispersion of complexity within knowledge levels indicates how useful that factor could be

in predicting wages independently of

knowledge. To take the most extreme

example, one wouldn’t expect complexity to explain much of the observed

wage variance in a sample of knowledge level 1 jobs, simply because there

is little complexity variation in that

sample. On the other hand, complexity could potentially explain a great

deal of the observed wage variance in

a sample of knowledge level-5 jobs.

Generic leveling factors as wage

determinants

Wage rates vary substantially by industry, location, occupation, union status, full- and part-time employment,

and establishment employment size.1

These wage predictors explain much

but not all of the observed wage variation. This section compares the predictive power of the generic leveling

data to that of other wage predictors.

Table 4 gives R-squared values for

a battery of log wage regressions.2

Statistics are presented first for more

traditional variables and the less traditional generic leveling factors data

taken together and then for each set

separately. The first number in table

4, .847, indicates that almost 85 percent of the wage variation in these data

can be explained by the generic leveling factors in combination with the

more traditional covariates.

Controls for the traditional job and

establishment attributes, without the

generic leveling factors, explain 72.6

percent of the wage variation. Occupation is the most important of these

variables; controls for occupational

groups alone can explain about 59 percent of the wage variation in these

data.3 Other factors that alone tend

to explain a substantial amount of

wage variation are industry groups (as

measured by 2-digit SIC indicators),

establishment size (as measured by

employment), and ownership (whether

the establishment is private or operated by a State or local government).

On the other hand, union status has

little explanatory power in this Rsquared sense. Since most jobs are

nonunion, and because there are so

many factors affecting wage rates,

unionization by itself explains a small

fraction of overall wage variation.

Table 4 also gives R-squared values for a series of regressions where

the explanatory variables are indicator variables for the generic leveling

factors. The R-squared for knowledge

(.702) indicates that this factor alone

explains almost as much wage variance as do all of the traditional

covariates (.726). On the other hand,

some of the other generic leveling factors do not explain very much of the

overall wage variance. For example,

supervisory duties alone would explain

about 18 percent of the wage variance.

The same explanation applies here as

with the union status variable. Even

if there are large differences in wages

for different levels of supervisory duties, that variable explains a relatively

small portion of wage variance because

there is only a small amount of variation in the explanatory variable and a

large amount of variation in wages.

The generic leveling factors explain

about 75 percent of the wage variance,

somewhat more than the traditional

covariates at about 73 percent. If one

had to choose between the set of traditional covariates and the set of generic

leveling variables, with the sole purpose of maximizing predicted wage

variance, one would choose the generic

leveling data. Given that combining

the generic leveling data and the traditional covariates in the same regression raises the R-squared to .847, it

appears that each set of variables has

something to offer that the other does

not, and that they at least partially

measure different things.

Looking beyond the R-squared statistics, the regressions indicate the

wage differentials associated with each

explanatory variable. For example,

they show the measured union wage

premium when other factors are controlled for. Or, the regressions show

the wage premium associated with jobs

having knowledge level 2 rather than

knowledge level 1, controlling for

other factors. Table 5 gives some inTABLE 2. Rank order correlation coefficents of generic leveling factors, 1997 National Compensation Survey

Knowledge ........................................ 1.00 - --------

Supervision received ......................... .83 1.00 --------

Guidelines ........................................ .82 .85 1.00 -------

Complexity ........................................ .84 .85 .85 1.00 ------

Scope and effect ............................... .82 .84 .87 .85 1.00 -----

Personal contacts ............................. .72 .64 .60 .60 .60 1.00 ----

Purpose of contacts .......................... .74 .67 .65 .64 .64 .74 1.00 - - -

Physical demands............................. -.45 -.32 -.30 -.31 -.25 -.51 -.41 1.00 - -

Work environment ............................. -.33 -.21 -.18 -.18 -.14 -.47 -.35 .78 1.00 -

Supervisory duties ............................ .47 .47 .45 .44 .44 .39 .46 -.16 -.12 1.00

NOTE: Dash indicates redundant data.

Personal

contacts

Complexity

Scope

and

effect

Supervision

received

Supervisory

duties

Purpose

of

contacts

Generic leveling factor Knowledge

Guidelines

Work

environment

Physical

demands
 Compensation and Working Conditions Winter 1999 11

Predicting Wage Rates

dication of the premiums attached to

a subset of these variables. Two regressions are presented. The first includes the usual job and establishment

attributes. The second includes all generic leveling factors as well as the

usual job and establishment attributes.

In subsequent tables, the former regression is referred to as “including

job and establishment attributes” and

the latter is referred to as “including

job and establishment attributes plus

generic leveling factors.”

As expected, wage rates are positively related to establishment size,

union status, full-time status, and incentive pay, even controlling for other

factors. Because the dependent variable in these regressions is the natural

logarithm of the hourly wage rate, coefficients may be interpreted as approximate percentage differentials.

The union status, full-time status, and

incentive pay variables are all indicator variables (meaning they take the

value 1 when the category applies, and

0 when it does not), so that the interpretation of the coefficients as approximate percentage differentials is

straightforward. For example, the

union status coefficient of .168 indicates that wages in union covered jobs

are about 17 percent higher than those

in nonunion jobs, after controlling for

the other covariates listed.4 Because

the establishment size variable is continuous, it may help to report the predicted wage difference associated with

some fixed difference in establishment

employment size. A one standard deviation change in the natural logarithm of establishment employment is

1.67, which implies a predicted wage

change of 1.67 times .030, or about 5

percent.5 The wage premiums listed

in both columns are fairly large, and

all are estimated precisely (as evidenced by relatively small standard

errors). Note that the coefficients from

the regression including job and establishment attributes are generally

smaller than the corresponding ones

from the regression including job and

establishment attributes plus generic

leveling factors, meaning that controlling for differences in the generic leveling factors tends to reduce the estimated wage premiums of other

variables.6

Table 5 does not present all of the

wage coefficient estimates. However,

because the effects of the generic leveling data are of some independent

interest, those results are given in table

6. The regression including job and

establishment attributes plus generic

leveling factors has 40 variables that

describe the generic leveling information—each generic leveling factor has

a set of indicator variables associated

with it. Estimated coefficients and

standard errors for these 40 variables

are presented on a factor by factor basis in table 6. In all cases, the variables describe the effect on wages of

having a higher level of some factor,

as opposed to having the lowest possible level of that factor. For example,

the first number in table 6 is 0.089,

for the second level of knowledge.

This indicates that jobs with knowledge level 2 are estimated to have

wages about 9 percent higher than otherwise comparable jobs with knowledge level 1.7 The standard error of

0.009 indicates that the 9 percent point

estimate is rather precisely estimated,

TABLE 3. Employment distribution by complexity levels across knowledge levels, 1997

National Compensation Survey

(Percent)

1 ......................................... 95.4 35.2 0.9 0.1 0.3 0000

2 ......................................... 4.6 61.9 60.9 21.7 33.8 7.2 .1 0 0

3 ......................................... 0 2.9 38.1 74.3 50.4 85.8 18.5 .2 0

4 ......................................... 0 0 .1 3.9 15.5 6.9 59.0 6.5 1.6

5 ......................................... 0 0 0 0 0 .1 21.9 88.3 19.3

6 ......................................... 0 0 0 0 0 0 .4 5.0 79.2

Knowledge marginal

 distribution ...................... 13.1 28.0 18.7 11.9 6.6 13.9 6.3 1.5 .1

TABLE 4. Variance results by job and

establishment attributes and generic

leveling factors, 1997 National

Compensation Survey

All job and establishment

 attributes plus generic

 leveling factors ................. .847

All job and establishment

 attributes .......................... .726

Occupational group .............. .590

Full-time status .................... .132

Industry group ...................... .303

Establishment

 employment size .............. .089

Ownership ........................... .050

Survey locality ...................... .069

Union status ........................ .041

Incentive pay ........................ .000

All generic leveling factors ... .749

Knowledge ........................... .702

Supervision received ............ .626

Guidelines ........................... .623

Complexity ........................... .625

Scope and effect .................. .605

Personal contacts ................ .386

Purpose of contacts ............. .430

Physical demands................ .170

Work environment ................ .076

Supervisory duties ............... .179

 1 Proportion of wage variance explained by

the indicator variables.

 NOTE: Statistics are R-squared numbers

from regressions of log wages on the indicated

characteristics. With the exception of employment size, all job and establishment attributes

are represented by indicator variables. Establishment employment size is continuous

and is entered in log form. The occupational

classification has 42 groups; the industry classification has 75 groups. Generic leveling data

are entered as indicator variables for each

level of the separate factors.

Knowledge level Complexity level

1 2 3 45 6789 R-squared1

Job and establishment

attributes plus

generic leveling factors

NOTE: The first six rows give the distribution of the complexity factor conditional on

having the given value for knowledge. For

example, 95.4 percent of knowledge level = 1

jobs have complexity level = 1, and 4.6 percent of knowledge level = 1 jobs have complexity level = 2. Hence the numbers in the

first six rows sum to 100 percent for each

column. The final row gives the percent of

jobs with the given level of knowledge. For

example, 13.1 percent of the jobs have knowledge level = 1. Therefore the final row sums

to 100 percent across columns. Due to rounding, sums of percentages may not equal 100.
12 Compensation and Working Conditions Winter 1999

and is statistically different from zero.

The coefficient estimate of 0.171 for

knowledge level 3 means that wages

among those jobs are approximately

17 percent higher than are wages

among otherwise comparable jobs with

knowledge level 1. The approximate

premium for moving from knowledge

level 2 to knowledge level 3 is about 8

percent (0.171-0.089 = 0.082).8

In fact, the estimated premiums

from increasing knowledge one level

is routinely on the order of 8 to 15 percent, except at the very top end of the

knowledge scale. These premiums are

comparable to or slightly lower than

those for union status, full-time status, and incentive pay. For example,

the full-time status wage premium of

0.120 from table 5 is about equal to

the wage differential associated with

having knowledge level 7 rather than

knowledge level 6.

The other generic leveling factors

tend to exhibit lower wage premiums,

and some factors tend not to be very

useful in predicting wages once other

factors are controlled for. The wage

premium for an additional level of supervisory control is about 8 percent

throughout its range. The estimated

premium associated with a higher level

of guidelines is about 5 percent at

lower levels and somewhat more at

higher levels. The analogous estimates for complexity are 3 to 5 percent at lower levels and closer to 10

percent at the highest level, although

those effects are less precisely estimated than some of the other coefficients. In this regression, the scope

and effect, personal contacts, purpose

of contacts, work environment, and

physical demands factors tend to have

small and sometimes imprecisely estimated coefficients. The wage return

to higher supervisory duties is small

at lower levels but fairly substantial at

higher ones. In interpreting these results, note that the very top levels of

these factors are sparsely populated.

The larger standard errors for the toplevel coefficients reflect that sparseness. Nevertheless, it appears that several of the generic leveling factors are

useful in predicting wages, at least as

compared to the more traditional

covariates. For example, the wage premium associated with two extra levels

of supervisory control is about the same

as that associated with union status.

Between- and within-occupational variation

Generic leveling data are meant to

capture differences across jobs in job

duties and tasks. Do occupational

identifiers also capture those differences across jobs? A comparison of

the data in table 5 shows that job and

establishment attributes combined

with generic leveling factors contain

data not captured by job and establishment attributes alone. Just how do the

generic leveling factors relate to occupation?

Table 5 gave results from two different regressions. Both regressions

control for job and establishment attributes, which include indicator variables for occupational group. The difference between the two regressions is

that one controls for the generic leveling factors while the other does not.

If the generic leveling factors and occupational indicators both capture differences across jobs in job duties, then

the coefficients on the occupational

indicator—which are interpretable as

occupational wage premiums—are

likely to be quite different in the two

regressions. For example, engineering jobs may pay quite a bit more than

other jobs, but they may not pay more

than other jobs with comparable levels of knowledge and other generic

leveling factors.

Table 7 gives the estimated occupational log wage premiums corresponding to each of these two regressions. The statistics in the column

labeled “Premium” describe wage differences between occupations. The

first number in table 7, .620, is the

estimated occupational premium for

public administration officials from

the regression including job and establishment attributes. This statistic

reflects a very large wage rate for public administration officials relative to

the average occupation, even after controlling for the traditional covariates

in table 5.9 The premium of -.080 for

the same occupation from the regression including job and establishment

attributes plus the generic leveling factors indicates that public administration officials actually are estimated to

earn about 8 percent less than the average occupation, once the generic leveling factors are also controlled for.

This implies that much of the difference in wage rates between the average occupation and public administration jobs is attributable to differences

in the generic leveling factors. In fact,

most of the estimated wage differentials shrink dramatically once controls

are instituted for the generic leveling

factors. This is reassuring from the

standpoint of data collection, because

it suggests that the generic leveling

TABLE 5. Wage regression coefficient estimates and standard errors for selected job

and establishment attributes, 1997 National Compensation Survey

Log of establishment employment size ....... 0.030 0.002 0.021 0.002

Union status .............................................. .168 .008 .160 .006

Full-time status .......................................... .234 .006 .120 .005

Incentive pay .............................................. .159 .015 .107 .011

Regression includes—

Coefficient

Job and establishment

attributes plus

generic leveling factors

Standard

error

Standard

error

Job and establishment attribute

Job and establishment

attributes

Coefficient

NOTE: The dependent variable is the average log wage of incumbent workers in the

sampled job. Both regressions include indicator variables for ownership, detailed industry classification (2-digit SIC), detailed occupational group, and survey locality. The

second regression also includes indicator variables for levels of each generic leveling factor. Standard errors are robust, and include

corrections for clustering by establishment.

Within 17,246 establishments, 145,054 observations were made.
 Compensation and Working Conditions Winter 1999 13

Predicting Wage Rates

data, in large part, measure one of the

things they were designed to: Differences across occupations in job duties.

One conclusion to be drawn from

table 7 is that occupational detail and

generic leveling data to some extent

represent substitute information. Is the

generic leveling information also valuable for within-occupation wage determination? We know that knowledge and the other factors help predict

wage differences between, say, engineers and sales representatives, but do

they also help us predict wage rates

among engineers?

The variance columns in table 7

address within-occupational variances.

These statistics are constructed as follows. The wage regressions from table

5 imply predicted log wage rates for

each observation in the data. The actual and predicted wage rates will of

course differ since the regressions do

not predict perfectly. The difference

between the actual and predicted wage

rate is usually termed a “residual” or

unexplained component. The statistics in the table 7 column labeled

“Variance” are the variances of that

residual component among the observations in each occupational category.

The regressions differ only in that one

includes the generic leveling factors

as explanatory variables and the other

does not. If the generic leveling factors help predict wage differences

within occupational group, then the residual components based on the regression including job and establishment

attributes plus generic leveling factors

will have smaller variances. Among

public administration officials, for example, the residual wage variance is

.149 based on the regression that does

not include generic leveling factors

and .077 based on the regression that

does. That is, even among like jobs

the generic leveling factors help exTABLE 6. Wage regression coefficient estimates and standard errors for generic leveling factor levels, 1997 National Compensation

Survey

1

Coefficient............................ ----------

Standard error ...................... ----------

2 ............................................

Coefficient............................ 0.089 0.081 0.060 0.029 0.029 -0.012 0.027 -0.023 0.036 0.017

Standard error ...................... .009 .009 .007 .006 .007 .005 .005 .007 .007 .005

3 ............................................

Coefficient............................ .171 .156 .104 .078 .053 .018 .038 -.017 .062 .059

Standard error ...................... .012 .011 .010 .009 .009 .009 .009 .013 .017 .009

4 ............................................

Coefficient............................ .302 .234 .173 .096 .074 .071 .027 - - .116

Standard error ...................... .012 .015 .014 .013 .013 .031 .027 - - .013

5 ............................................

Coefficient............................ .451 .318 .265 .150 .162 ---- .320

Standard error ...................... .016 .028 .030 .018 .031 ---- .033

6 ............................................

Coefficient............................ .590 - - .265 .078 -----

Standard error ...................... .018 - - .055 .049 -----

7 ............................................

Coefficient............................ .713 ---------

Standard error ...................... .021 ---------

8 ............................................

Coefficient............................ .818 ---------

Standard error ...................... .033 ---------

9 ............................................

Coefficient............................ .806 ---------

Standard error ...................... .059 ---------

Coefficient and standard error by

generic leveling factor level

plain a substantial amount of wage

variation.

The regression results listed thus

far employ an occupational classification scheme with about 40 groups. It

is also possible to investigate withinoccupational wage variation using a

finer occupational classification. To

carry out that investigation a very detailed occupational classification system is used, the 1990 census system,

which has approximately 470 occupations. Table 8 shows the knowledge

distributions within the 10 most populous of these occupations. For example, about 2.6 percent of the data

are for registered nurses, and 75 percent of registered nurses are coded as

having knowledge level 6. The knowledge variance within occupation is

smaller than the knowledge variance

in the sample as a whole. Nonetheless, there are some within-occupation

differences. Most of the occupations

Supervision

received

Complexity

Scope and

effect

Personal

contacts

Purpose

of

contacts

Physical

demands

Work

Environment

Generic leveling factor

KnowledgeGuidelines

Supervisory

 duties

NOTE: This table presents estimated coefficients for generic leveling indicator variables from the log wage regression including job and

establishment attributes plus generic leveling factors. Standard errors

are robust, and include corrections for clustering by establishment.

Dash indicates data not applicable.
14 Compensation and Working Conditions Winter 1999

TABLE 7. Occupational wage premiums and within-occupation residual wage variances, 1997 National

Compensation Survey

Executive, administrative, managerial

 Public administration officials ................................................. 0.620 0.149 -0.080 0.077

 Other executives and managers ............................................. .774 .162 .003 .056

 Management-related occupations .......................................... .358 .102 -.029 .042

Professional specialty occupations

 Engineers, architects and surveyors ...................................... .606 .077 .014 .033

 Mathematical and computer scientists ................................... .644 .105 .062 .041

 Natural scientists ................................................................... .438 .123 -.114 .057

 Health diagnosing occupations .............................................. 1.00 .557 .094 .293

 Health assessment and treating ............................................. .539 .066 .062 .045

 Postsecondary teachers......................................................... .871 .146 .109 .097

 Other teachers ....................................................................... .550 .095 .158 .066

 Lawyers and judges ............................................................... .842 .169 .027 .070

 Other professional specialty ................................................... .333 .130 -.066 .063

Technicians

 Health technologists and technicians ..................................... .125 .076 .026 .039

 Engineering and science technicians ..................................... .163 .076 .028 .032

 Other technicians ................................................................... .343 .240 .090 .111

Sales occupations

 Sales supervisors and proprietors .......................................... .277 .182 -.007 .077

 Sales, finance and business .................................................. .215 .258 .073 .113

 Sales, commodities excluding retail ........................................ .386 .182 .104 .104

 Sales, retail and personal services ......................................... -.247 .076 -.032 .049

 Other sales-related occupations ............................................. -.160 .092 -.029 .040

Administrative support

 Supervisors, administrative support ....................................... .215 .060 -.068 .040

 Computer equipment operators .............................................. -.061 .065 .032 .034

 Secretaries, typists ................................................................ -.061 .057 .053 .033

 Financial records processing ................................................. -.112 .056 .031 .031

 Mail and message distributing ................................................ -.373 .061 .010 .033

 Other administrative support .................................................. -.213 .061 -.001 .035

Service occupations

 Protective service ................................................................... -.049 .116 -.030 .047

 Food service .......................................................................... -.439 .139 -.159 .115

 Health service ........................................................................ -.322 .048 -.076 .032

 Cleaning and building service................................................. -.358 .057 -.028 .039

 Personal service .................................................................... -.303 .128 -.076 .061

Precision production, craft, repair

 Mechanics and repairers ........................................................ .090 .069 .018 .037

 Construction trades ................................................................ .109 .076 .035 .039

 Other precision production ..................................................... -.041 .111 -.003 .037

Operators, fabricators, laborers

 Machine operators and tenders .............................................. -.247 .067 -.002 .039

 Fabricators, assemblers, inspectors ...................................... -.297 .075 -.002 .054

 Motor vehicle operators .......................................................... -.120 .071 .091 .050

 Other transport and material moving ...................................... -.123 .075 .065 .045

 Construction laborers ............................................................. -.319 .072 .064 .040

 Freight, stock, and material handlers...................................... -.327 .057 .006 .044

 Other handlers and laborers ................................................... -.376 .073 -.021 .045

 Farm-related occupations ...................................................... -.234 .069 .041 .030

 Regressions include—

Job and establishment

attributes

Job and establishment

attributes plus generic

leveling factors

Occupation

Premium1 Variance2 Premium1 Variance2

1 Occupational wage premium. Log wage differentials

between the given occupation and the average occupation.

 2 Within-occupation residual wage variance. Variance of

regression residuals within the stated occupational category.
 Compensation and Working Conditions Winter 1999 15

Predicting Wage Rates

listed in table 8 have a clear modal

knowledge level (that is, one knowledge level stands out). Typically,

neighboring knowledge levels are substantially populated as well. Although

based on a few sample occupations,

table 8 suggests non-trivial within-occupational differences in the generic

leveling data.

Table 9 shows that the within-occupation differences in the generic leveling data can explain some of the

within-occupation wage differences. It

shows the results of comparing the

variance of predicted wages within a

census occupational category to the

variance in actual wages within the

same category. Predicted wages are

based on a regression of log wages on

census occupational and generic leveling indicators.10 Because the generic

leveling factors do not explain wages

perfectly, the variance in predicted

wage is smaller than the variance in

actual wages. Therefore, the ratio of

the predicted wage variance to the actual wage variance within the occupation forms an index of how well the

generic leveling data predict wages

within that occupation. This ratio is

averaged for all occupations taken together, as well as for broad occupational categories. For all 468 census

occupations in the sample, the generic

leveling data explain, on average, 42

percent of the within-occupation wage

variance.

A final question relates to the nature of the occupations for which the

generic leveling data predict wages

well. One would not necessarily think

that these data would be as helpful in

predicting wages within, for example,

a group of janitors, as within a group

of accountants. The nature of the generic leveling data lead one to suspect

that they are more able to distinguish

among similar white-collar workers

than to distinguish among similar

blue-collar workers.11 For professional occupations, on average, about

half of the wage variation within a

very narrowly defined occupation can

be accounted for by variation in the

generic leveling data. The analogous

fractions for technical occupations and

executive occupations are quite a bit

higher than even that. On the other

hand, the generic leveling data explain a small fraction of the withinoccupation wage variation for bluecollar and service occupations. This

indicates that the generic leveling data

is more useful in distinguishing

among white- than blue-collar or service sector jobs, at least when defining jobs using the census occupational

classification system.

Summary

• Jobs with higher levels of generic leveling factors tend to have

substantially higher wage rates.

This is especially true for cognitive and managerial-related

factors.

• Along with covariates traditionally used in labor economics research (such as industry, union

or full-time status, etc.), generic

leveling data explain a high proportion of the observed wage

variation.

• When included as additional controls in wage equations, generic

leveling data tend to be lower

than the estimated wage differentials associated with some of

the more traditional establishment survey wage determinants,

such as establishment size and

industry differentials.

• Additionally, generic leveling

data explain wage differences across occupations very well, and

explain more of the wage differences within white-collar occupations than within blue-collar

occupations.

TABLE 8. Employment distribution by knowledge level across selected occupations, 1997 National Compensation Survey

1 ................................................... 0 55.0 37.2 0 5.1 0.1 0 7.0 24.0 56.8

2 ................................................... 0 36.1 54.3 .1 72.7 13.5 0 41.9 61.1 38.1

3 ................................................... .3 7.9 7.9 0 20.8 57.5 .1 43.1 13.5 4.8

4 ................................................... 1.4 1.0 .4 .1 1.2 28.5 .9 7.9 1.3 .3

5 ................................................... 16.4 0 .2 18.4 .2 .4 6.2 .1 .1 0

6 ................................................... 75.0 0 0 79.5 .1 0 31.6 0 0 0

7 ................................................... 6.8 0 0 1.9 0 0 42.9 0 0 0

8 ................................................... .1 00000 17.2 0 0 0

9 ................................................... 0 0 0 0 0 0 1.1 0 0 0

Percent of workers in occupation ....... 2.59 2.51 2.50 2.31 2.31 2.21 2.01 2.00 1.80 1.72

Knowledge level

Selected occupation

Registered

nurses

Janitors

and

cleaners

Cashiers

Elementary

school

teachers

Nursing

aids,

orderlies

and

attendants

Secretaries

Managers

and

administrators

(n.e.c.)

General

office

clerks

Assemblers

Stock

handlers

and

baggers

1

1

 Percent of the 1997 National Compensation Survey sample weight

in the given occupation.

NOTE: n.e.c. means “not elsewhere classified.”
16 Compensation and Working Conditions Winter 1999

1 Classic references include Charles Brown

and James Medoff, “The Employer Size-Wage

Effect,” 97(5), Journal of Political Economy,

October 1989, pp.1027-1059; Alan B. Krueger

and Lawrence H. Summers, “Efficiency Wages

and the Wage Structure,” Econometrica, 56(2),

March 1988, pp. 259-293; and H. Gregg Lewis,

Union Relative Wage Effects: A Survey, University of Chicago Press, Chicago, 1986. 2

 R-squared measures the proportion of the

wage variance explained by the regression’s independent variables. In all regressions, the dependent variable is the average (across job incumbents) of the natural logarithm of hourly wages in

the sampled job. Hence the R-squared statistics

presented do not include the wage variation within

sampled jobs, which is small relative to that across

sampled jobs. 3

 The classification uses 42 occupational categories.

TABLE 9. Mean fraction of within-occupation wage variance explained by generic leveling factors

by occupational categories, 1997 National Compensation Survey

All occupation average ....................................................................... 468 .42

 White collar

Professional ............................................................................ 100 .48

Technical and related .............................................................. 22 .65

Executive, administrative, and managerial ............................... 26 .58

Sales ...................................................................................... 22 .39

Administrative support, including clerical................................. 54 .42

 Blue collar

Precision production, craft, and repair ..................................... 98 .46

Machine operators, assemblers, and inspectors ..................... 56 .39

Transportation and material moving ......................................... 24 .30

Handlers, equipment cleaners, helpers, and laborers .............. 27 .27

 Service

Service, except private household ........................................... 39 .35

Number of

occupations Occupational category Mean fraction

4

 More precisely, the estimated differential is

exp (.168)-1 = .183, or 18.3 percent. Wage regression coefficients are reported throughout the

text and then interpreted as approximate differentials. This practice is less precise but facilitates

references to the tables. 5

 These surveys are restricted to establishments

of at least 50 workers. Therefore, the estimated

establishment employment coefficient does not

reflect the wage-employment relationship in small

establishments. 6

 For instance, to the extent that generic leveling data capture worker abilities, the differences

in the regression coefficients in table 5 suggest

that observed wage differences by establishment

size or full-time status do partly reflect worker

abilities, whereas wage differences due to union

status do not. 7

 “Otherwise comparable” here means controlling for industry, occupation, establishment employment size, generic leveling factors, and all of

the other covariates listed in table 5. 8

 As indicated in footnote 4, the precise premium is calculated as exp (.171) / exp (.089) minus 1, which equals .085, or 8.5 percent. 9

 More precisely, estimated wages in this occupation are exp (.62) which is 1.86 times as large

as for the average occupation after controlling for

other job and establishment attributes. Premiums

in table 7 are the log wage regression coefficients

from table 5 normed to be relative to the average

occupational premium (instead of an omitted occupational category). 10 Note that the regression pools different occupations, so the estimated returns to knowledge and other factors are not allowed to vary by

occupation. 11 The Office of Personnel Management originally designed generic leveling factors for the

purposes of pay setting for white-collar Federal

employees.

NOTE: Data in this table are derived from a log

wage regression on 3-digit census and generic leveling indicator variables. For each 3-digit census

occupation, the fraction of the within-occupation

wage variance explained by the generic leveling factors is defined as the variance of the regression predicted values divided by the variance of the actual

log wage rates. The column labeled “mean fraction” presents the weighted average of this fraction

across the occupations in the stated occupational

group, with weights determined by NCS sampling

weights.