22 Monthly Labor Review October 2001

 Wage Query System Wage Query System

The search for wage data can be

daunting. Data are available for different job characteristics, such as occupation, industry, or geographic area; by demographics of the wage earner, such as race,

sex, education, or age; and in a variety of

forms, such as hourly wages, annual salaries,

total employer payrolls, gross pay, or net pay.

Beyond these variations, users of wage data

may ask how wage is defined in the measure. Is it straight time or does it include

overtime? Are other cash payments, such as

commissions or year-end bonuses, included?

Finally, how reliable are the data? Have they

been subjected to the scrutiny of statistical

methods? Do they include sufficient observations to support generalizations about

wages in the marketplace?

There is no panacea to simplify these complexities or to ensure appropriate application of

available data. Users of wage data are advised

to learn as much about their data source as possible—in particular, whether the data use the

appropriate definition and meet the standard

of quality required for their purpose. The Bureau of Labor Statistics publishes a number

of different wage measures. To enable data

users to find hourly wage data more easily,

BLS recently added a new feature to its

Internet site—the wage query system. This

interactive application allows users to request

wage data from the National Compensation

Survey (NCS) by certain characteristics. Once

they have targeted the specific data, the results are returned almost instantly.

The BLS wage query system:

a new tool to access wage data

A new Internet tool available on the BLS website

makes it easier than ever to access

wage data by area, occupation, and work level

Maury Gittleman

and

William J. Wiatrowski

Maury Gittleman is an

economist in the Compensation Research and Program Development Group

(call 202–691–6318, or email

at gittleman_m@bls.gov).

William J. Wiatrowski is a supervisory economist in the

Division of Compensation

Data Analysis and Planning

(call 202–691–6255, or email

at wiatrowski_w@bls.gov).

This article provides information on the data

behind the new query system, a section on navigating the system, and a discussion on the new

regression estimates that recently were added

to the query system. Regression estimates help

to provide more complete data on area wages

by occupation and level of work, an important

component of the wage query system. The article concludes with a look at enhancements

planned for the future.

Data drive the wage query system

The data behind the new query system come

from the National Compensation Survey,

which is a BLS survey of wages and benefits

throughout the United States. Although the

NCS database includes employer costs for

wages and benefits, rates of change in those

costs, and detailed information on benefit

plans, this discussion is limited to the query

of hourly wage rates.1 BLS also publishes

other wage measures, each with its own

unique characteristics. The feature that sets

the NCS wage estimates apart from other wage

data currently available is information on

“work level.” Not only can data users

search for the average wage of, for example,

accountants in Los Angeles, they also can

select by work level to view average wages

of entry level or senior accountants in that

locality.

The wage query system presents data

tabulated in the same manner as all NCS publications. The user is asked to select among
Monthly Labor Review October 2001 23

• Guidelines

• Scope and effect

• Personal contacts

• Purpose of contacts

• Physical demands

• Work environment

For example, there are several possible levels of knowledge,

ranging from the knowledge of simple, routine, or repetitive tasks

to mastery of a professional field to generate and develop new

hypotheses and theories. Points are associated with each level

of each job characteristic; the sum of the points for all characteristics determines the overall work level of the occupation. (See

exhibit 1 for a complete description of the work level system.)

Presently, wage data are published by occupation and

work level, using work levels that correspond to the Federal General Schedule pay system of 15 grades, numbered

1 to 15.4 Research is underway to determine alternate

groupings for publishing data by work level, in an effort to

make the distinction between grades more meaningful. For

example, several of the lower grades may be combined into

an “entry level” category, while upper grades may be combined into a “senior level” category.

Navigating the wage query system

The wage query system is an interface on the BLS Internet

website that prompts the user to enter an area, an occupation, and a work level to retrieve an estimate of the average

hourly wages derived from NCS data. The query system is

located in the NCS section of the BLS website (www.bls.gov) at

http://data.bls.gov/labjava/outside.jsp?survey=nc. On the entry screen, the user first selects an area and an occupation. The

query system displays only those areas and occupations for

which data are available. The mechanism for entering an area

and an occupation are related. If the user chooses an area, the

occupation list will show only those occupations for which data

are available for that area. Similarly, if the user chooses an occupation, the area list will show only those areas for which data are

available for that occupation. These features may be helpful if a

user is attempting to find wage data for multiple occupations in

the same area or for the same occupation in multiple areas.

Once the user has selected an area and occupation, he or she

may select a work level. If wage data by work level are not needed,

the automatic default selection is “Overall occupation average

(no work level).” At that point, the user can view wage data for

the selected area and occupation. If the user needs wage data

by work level, he can either designate a specific work level or

build a work level by defining each of the nine key job characteristics. In either case, once the work level is determined,

the user can view wage data for the selected area, occupation, and work level.

choices of area, occupation, and work level. To understand

the selections the query system offers, it is helpful to review

basic features of the NCS survey and its publications.

Area. The NCS is an area-based survey, meaning data are

collected only in selected areas of the country, which are designed to represent all areas of the country. The current NCS

sample of areas is made up of 154 areas—81 metropolitan

areas and 73 nonmetropolitan counties. Wage data are published for about 90 areas annually, including most of the metropolitan areas and a small number of nonmetropolitan counties. The areas are also designed to represent nine broad geographic regions and to represent the United States as a whole.

Wage data are published annually for the nine regions and

for the United States.

Occupation. Data are collected from a sample of employers

within the 154 areas. BLS economists visit these employers

and obtain wage and benefit information from a sample of

occupations within the establishments. These occupations

are classified into one of 480 occupations, based on the duties and responsibilities of the job.2 Occupations are narrowly defined—there are 13 different categories for engineers, for example, ranging from civil and industrial to petroleum and aerospace engineers. Data are published for as

many occupations as possible, given that data exist and that

they meet confidentiality and reliability standards.3 In many

large areas, data are published for 150 to 175 occupations; for

the United States, data are published for about 450 occupations.

The occupational classification system used to define

each job is hierarchical, which means that each detailed occupation is part of larger and larger groupings. The civil engineer occupation, for example, is part of the larger group engineers, architects, and surveyors, which in turn is part of the

still larger group professional specialty occupations. That

group is part of the composite group professional specialty

and technical occupations, which is part of white-collar occupations. Finally, this last category is part of the much larger

“all workers” group. If data are not available for a specific

detailed occupation, the user may be able to find data for a

larger grouping that incorporates that occupation.

Work level. In addition to classifying each occupation on

the basis of duties and responsibilities, BLS economists also

determine the work level of the occupation. This is intended

to differentiate between workers within the same occupation.

The level of work is determined by assessing the following

nine key job characteristics:

• Knowledge

• Supervisory controls

• Complexity
24 Monthly Labor Review October 2001

Wage Query System

 Exhibit 1. Description of work level system

A sample of occupations is selected from each establishment in

the National Compensation Survey (NCS). BLS then collects

information on the duties and responsibilities involved in these

occupations in order to classifiy them into the appropriate detailed occupational categories. In addition, the work level of

each selected occupation is determined using the U.S. Office of

Personnel Management’s Factor Evaluation System, which is

the underlying structure for evaluation of Federal General Schedule (GS) employees. The following list includes a brief description of each of the factors:

Knowledge measures the nature and extent of information or facts

that the workers must understand to do acceptable work and the

nature and extent of the skills needed to apply those knowledges.

Supervision received covers the nature and extent of direct or

indirect controls exercised by the supervisor, the employee’s

responsibility, and the review of completed work.

Guidelines covers the nature of instructions, procedures, and

directions and the judgment needed to apply them.

Complexity covers the nature, number, variety, and intricacy of

tasks, steps, processes, or methods in the work performed; the

difficulty in identifying what needs to be done; and the difficulty

and originality involved in performing the work.

Scope and effect covers the relationship between the nature of the

work (purpose, breadth, and depth of assignment) and the effect

of work products or services both within and outside the organization.

Personal contacts includes face-to-face contacts and telephone

dialogue with persons not in the supervisory chain.

Purpose of contacts ranges from factual exchanges of information

to situations involving significant or controversial issues and differing viewpoints, goals, or objectives.

Physical demands covers the requirements and physical abilities

required by the employee to complete the work assignment.

Work environment considers the risks and discomforts in the

employee’s physical surroundings or the nature of the work assignment and the safety regulations required.

Within each factor are a number of levels, and each level has

an associated written description and point value. The number and range of points differ among the factors. For each

NCS occupation, the level and associated point value of each

factor is determined on the basis of occupation position descriptions and interviews with survey respondents. The

point values are recorded and totaled; the total points determine the overall level (or grade) of the occupation, based on

the same 15 levels used for the Federal Government’s General Schedule employees. A description of the levels for each

factor can be found within the BLS website at the following

address: www.bls.gov/ncs/ .bls.gov/ncs/.

Using regression techniques, BLS researchers examined the

relationship between wages and the nine factors used to determine overall grade level. The analysis showed that several

of the factors, most notably knowledge and supervision received, had strong explanatory power for wages. That is, as

the levels within a given factor increased, the wages also

increased. For additional information see Brooks Pierce,

“Using the National Compensation Survey to Predict Wage

Rates,” Compensation and Working Conditions, Winter

1999, pp. 8–16.

Query limitations and complexities

In the NCS, available work levels vary by occupation. For

example, clerical workers typically are found in work levels

01 through 08. Alternatively, professional workers typically begin at work level 05 or 07 and can be as high as

work level 15. The query system prevents users from requesting data for a work level that is not appropriate for

the occupation. In addition, a few occupations—legislators, dancers, artists, athletes, authors, actors, musicians,

painters/sculptors, and announcers—are not classified by

work level. The Federal Government developed the Factor

Evaluation System used in the NCS for the evaluation of

white-collar workers. When BLS adopted this system for

the NCS, it reviewed the factors to determine their appropriateness for the occupations being surveyed. The nine

occupations excluded from the work level process were

thought to have other criteria that determined work level

and pay. Wage data are available for these occupations,

but not by work level.

In some cases, there are insufficient data to publish all

work levels for an occupation. For example, of the eight possible work levels for accountants in Miami, in a given year

fewer than eight are published. This occurs for two reasons.

First, the survey includes only a subset of the occupations in

each sampled establishment in a given area, rather than a

census of all jobs in every establishment. Second, data for

certain work levels may not meet BLS confidentiality and reliability standards. As of June 2001, estimates of average

wages for these “missing” work levels within occupations

can be obtained using regression models, as described in the

section that follows. Wage data by work level displayed in

the wage query system are derived either from direct estimation of data or from the regression model. This distinction is

clearly marked when users view results of their query.

Model-based estimates

Statisticians use direct estimation to produce the series of

average wages for area, occupation, and work level that ap-
Monthly Labor Review October 2001 25

pear in NCS publications and as part of the wage query system. This method, which refers to the direct computation of

an average (or other statistic) using sample data, is the technique used most often in BLS and other statistical agencies.

In some cases, however—often because the sample is too

small to produce reliable estimates—a different approach is

used: indirect estimation or model-based estimation.

5

To produce the indirect estimates of hourly wages by area,

occupation, and work level that now form part of the wage

query system, regression analysis is used. One important

aspect of regression methods is that they can be used to produce estimates of conditional means, which in this case refer to

the average hourly wage for individuals, given the area in

which they work, their occupation, and their work level.

Clearly, estimates of conditional means are generated by direct methods as well, but there are significant differences

between the two techniques.

Before discussing how the regression model works, it may

be useful to examine table 1, which displays statistics for hypothetical hourly wage data for three areas (X, Y, and Z), three

occupations (A, B, and C) and three work levels (1, 2, and 3).

The averages presented have been calculated by the usual

method of direct estimation. For the sake of simplicity, employment is distributed evenly across the cells (in the top

three panels of the table) that are defined by combinations of

these three dimensions. One can see, for example, that the

average wages of an individual in area X, occupation A, and

level 1 is $10.00.

The fact that both direct and indirect methods can be used

to produce conditional means makes it possible to use this

table to give a sense of how the regression model produces

its estimates. Before doing so, however, it may be helpful to

summarize some key patterns evident in the top three panels

of the table. First, for any given occupation and work level,

area Y tends to have the highest wages and area X the lowest

wages, while wages for area Z are somewhere in the middle.

Second, wages by occupation tend to be highest for occupation C and lowest for occupation A. Third, wages always

increase as the level of work increases.

To quantify these trends, one can take an average of the

cells by area, occupation, and work level, and then take an

average of all cells to obtain a mean for the Nation as a

whole. Taking one dimension at a time, one can then calculate differentials with respect to the overall average. For

instance, the wages for area X are, on average, $1.22 lower

than those for the Nation as a whole ($17.56 versus $18.78).

Similarly, the wages for occupation A are $2.56 lower than

the average for all occupations ($16.22 versus $18.78), while

those for work level 1 are $5.78 lower ($13.00 versus $18.78).

To provide a simplified example of how the regression

model works, let’s say one is interested in estimating an average wage for area X, occupation C, and work level 2. Instead

of using the direct estimate in the table, one can construct an

estimate in a fashion similar to the way the regression model

predicts wages. Using the numbers on the table and making the appropriate subtractions, one sees that average

wages in occupation C are $2.33 higher than the overall

average ($21.11 versus $18.78), and that those in level 2

are $0.11 higher ($18.89 versus $18.78). Remembering that

the wages in area X are $1.22 lower than the overall average, one can add the differentials to the national average

of $18.78, which results in a predicted wage of $20.00

($18.78 – $1.22 + $2.33 + $0.11 = $20.00).

In this case, the estimate computed indirectly via the model

exactly matches the $20.00 that resulted from a direct estimate. Even in this highly artificial example, however, most of

the wages predicted by the model would not be exactly right.

The reason is that the patterns of wages by occupation and

work level are not identical by area. That is, while table 1 was

constructed so that the ranking for pay of occupations and

work levels is the same for all areas, the exact magnitudes

sometimes differ. Thus, the implicit assumption of the model

that occupation and work level differentials are identical

across areas will, in general, lead to prediction errors.

The regression model used in the wage query system allows wages to differ by area and occupation as in this example. Instead of using work levels as a predictor, however,

the model uses scores on the nine factors that are used to

calculate the level. Although the example shows that prediction errors come from assuming that differences in wages by

occupation and by work level are the same across areas, the

regression model used does, in fact, make this assumption.

Table 1. Hypothetical mean hourly earnings by area,

 occupation, and work level

Area X Area Y Area Z Nation

Occupation A .........

Level 1 ................. $10.00 $12.00 $11.00 $11.00

 Level 2 ................. 15.00 18.00 16.00 16.33

 Level 3 ................. 20.00 23.00 21.00 21.33

...............................

Occupation B .........

 Level 1 ................. 12.00 14.00 13.00 13.00

 Level 2 ................. 18.00 20.00 19.00 19.00

 Level 3 ................. 24.00 26.00 25.00 25.00

...............................

Occupation C .........

 Level 1 ................. 14.00 16.00 15.00 15.00

 Level 2 ................. 20.00 22.00 22.00 21.33

 Level 3 ................. 25.00 28.00 28.00 27.00

...............................

Occupation A ......... 15.00 17.67 16.00 16.22

Occupation B ......... 18.00 20.00 19.00 19.00

Occupation C ......... 19.67 22.00 21.67 21.11

...............................

Level 1 ................... 12.00 14.00 13.00 13.00

Level 2 ................... 17.67 20.00 19.00 18.89

Level 3 ................... 23.00 25.67 24.67 24.44

Overall ................... 17.56 19.89 18.89 18.78

Items
26 Monthly Labor Review October 2001

Wage Query System

While the fact that this is not literally true introduces a greater

chance of prediction error, not making the assumption

means relying on smaller amounts of data to estimate how

these areas differ in this regard, which also increases the

chances of making inaccurate predictions.6 It should also

be noted that a variety of alternative models were assessed

that relaxed the assumption of equality of wage differences

by occupation and work level across areas. On average,

these models did not have better predictive power than

the model that was chosen for incorporation into the wage

query system.

Given these errors, one might naturally wonder why it is

useful to present estimates generated by the model. First, it is

important to keep in mind that even direct estimates contain

prediction errors. While they are correct, on average, for the

given sample, the average wage is, of course, not the wage

that everyone for that job actually receives. In fact, if one

could perform a parallel survey, where the respondents are

different because the establishments and the occupations

within the establishments that are randomly selected are different, the direct estimates also would undoubtedly differ.

Second, when using a model, one can combine data from

areas with similar labor market patterns to increase the sample

size, a process that statisticians refer to as “borrowing

strength.” While areas can be combined when making direct

estimates as well, a model has the advantage of being able to

incorporate the ways in which areas differ from each other.

Third, a model facilitates the incorporation of auxiliary information to improve the accuracy of its prediction. In this case,

using detailed information on factor scores, rather than the

work level, which is a kind of summary of the scores, improves the performance of the model.

While it is hoped that this description of where the modelbased estimates come from has been of interest (see the appendix for additional technical details), it is not necessary to

understand the details of the procedure for generating the

estimates in order to make good use of the data. It is important, however, that users know how to view the model-based

estimates relative to the directly estimated ones. First, the

regression-based estimates should be considered experimental. Though a substantial amount of work has gone into developing, estimating, and validating the model, and such

models have a long tradition in the field of labor economics,

it has not undergone the scrutiny given to standard BLS products and does not benefit from the years of experience BLS

has in direct estimation. Second, the regression-based estimates are being used only in cases where the sample size is

too small for direct estimates, indicating greater variability in

any estimate, direct or indirect. Work on the model is ongoing, and should, in the future, strengthen users’ confidence

in the regression-based estimates.

Future enhancements

The BLS wage query system has quickly become a popular

Internet tool—nearly 13,000 requests were processed

through the system in a recent month. The addition of regression estimates will only enhance the system’s usefulness. And BLS is researching additional enhancements as

well. Currently, the system is limited to the average wages for

all workers in the occupation. Future enhancements will allow users to obtain median and percentile wage estimates, as

well as iterations for private sector versus State and local

government, and full time versus part time. In addition, some

data will be available by union status, industry, and size of

establishment. Efforts also are underway to tie the output of

the query system to wage escalator calculations from the

Employment Cost Index.7 In this way, detailed occupational

wage estimates that may be several months old can be escalated to reflect wage rates in the most recent quarter.

1

 The earnings used to calculate the hourly wage rates are defined as regular payments from the employer to the employee as

compensation for straight-time hourly work, or for any salaried

work performed. Wage data represent gross pay (that is, prior to

taxes) and include incentive pay such as commissions and production bonuses, but do not include overtime or bonuses not directly

tied to production, such as hiring and year-end bonuses. For additional details, see National Compensation Survey: Occupational

Wages in the United States, 1999, Bulletin 2539 (Bureau of Labor

Statistics, July 2000). This information is available on the Internet

at www.bls.gov/ncs/.

2

 Occupations in the National Compensation Survey are defined

by the Census Occupational Classification System. The NCS is beginning to reclassify occupations using the new Standard Occupational Classification system. BLS expects to publish NCS wage data

with occupations defined using this new system by 2005.

3

 More precisely, for data in a given occupation to meet BLS publication standards, there must be sufficient observations to ensure that no

one establishment could be identified, perhaps because data from that

establishment dominate a particular estimate. In addition, the relative

standard error, calculated as the ratio of the standard error to the mean,

must be less than 0.50.

4

 The Federal General Schedule (GS) pay system is used for most

white-collar employees of the Federal Government.

5

 Examples of indirect estimation that will be familiar to many BLS

data users are the estimates produced by the BLS Local Area Unemployment Statistics (LAUS) program. These data and a description of the

estimation methodology may be found within the BLS Internet site at

www.bls.gov/lau/.

6

 The mean squared error, the measure used to gauge the level of

predictive accuracy, is composed of a term for prediction bias and one

for the variability of predictions. Restricting certain parameters to be

the same across regions imposes some bias, but decreases the variability of the estimates.

7

 The Employment Cost Index is a quarterly measure of the change

in employer costs for wages, salaries, and employer-provided benefits.

More information may be found at www.bls.gov/ncs/ect/.

NOTES
Monthly Labor Review October 2001 27

where Wm is the average hourly wage rate of the mth observation, which is for occupation o in an establishment that is in

area a and that has a vector f of scores for each of the nine

factors. AREA is a vector of dummy variables indicating area,

OCCUP is a vector of dummy variables for occupation, and

FACTOR is a matrix of dummy variables representing the different possible scores for each of the nine factors. The corresponding coefficients are β, χ, and δ, while α is a constant

term, and ε is the error term. Areas are indexed by a and are

numbered from one to A, occupations are indexed by o and

The model used to predict wages is of the form are numbered from one to O, i is the index for the nine factors,

while Si is the highest score possible for factor i. The coefficients are calculated by using weighted least squares. An

initial weight is determined for each observation by taking into

account the probability of selection for the establishment and a

given occupation in that establishment, and then corrected for

nonresponse. This final employment weight is then multiplied

by hours worked per week and weeks worked per year to arrive

at an hours weight.

Though it is conventional in labor economics to use the log

wage rather than the wage itself, taking logs did not improve the

performance of the model significantly. Many different specifications were tried, with most of the variations attempting to see

if the predictive accuracy of the model could be improved by

allowing either the coefficients on occupation, the coefficients

for the factor scores, or both, to vary by area. Using the measures root mean squared error and mean absolute error to

gauge predictive accuracy, it was not possible to find a model

that allowed occupation or factor score differentials to vary

by locality that substantially outperformed the model.

δ FACTOR ε

α β AREA χ OCCUP

9

1

1

1

1

1

1

1

mij m i

Si

j ij

mo

O

o ma o

A

a

Wm a

∑ ∑ +

= + ∑ + ∑ +

=

−

=

−

=

−

=

Appendix: Regression model