05/29/97

SAMPLE DESIGN AND ESTIMATION FOR THE

NATIONAL COMPENSATION SURVEY

Susan R. Black, Lawrence R. Ernst, Jason Tehonica

Bureau of Labor Statistics

2 Massachusetts Ave. N.E., Room 3160, Washington, DC 20212

1. Introduction

The National Compensation Survey (NCS) is a new statistical program that will

both replace the existing Occupational Compensation Survey (OCS) program and

integrate it with the Employment Cost Index (ECI) and the Employee Benefit Survey

(EBS) creating one comprehensive survey program. The OCS program publishes locality

and national occupational wage data used by the President’s Pay Agent and private sector

compensation specialists, among others. The OCS and ECI/EBS have been independent

samples, collected separately by regional field staff. These survey programs are being

combined because of a desire to lessen the respondent burden and to maximize the use of

limited resources. Similar to the OCS program, the NCS produces estimates of

occupational wages for Locality Pay and constructs national estimates from a probability

selection of establishments stratified geographically and by industrial activity. The NCS

also will maintain the current products of the ECI survey by producing national indexes

which track quarterly changes in labor costs, and also cost level information annually on

the cost per hour worked of each component of compensation.

The most important difference between the new NCS program and the old OCS

program is that the OCS program used a fixed list of occupations for which compensation

data were collected from all sampled establishments, thus publishing data for only a limited

number of occupations. The NCS uses a probability selection of occupations in each

establishment to insure a nearly universal coverage of occupations in the workforce. The

NCS will be able to publish estimates for a greater number of occupations, as well as

produce estimates for occupational groups. A second difference is that the NCS includes

establishments with at least one worker, while the OCS program only used establishments

with 50 or more workers.

While there are changes to the ECI arising from its integration with the NCS

program, the overall effect on the ECI will be minimal in terms of the estimates produced.

The main design difference is that the sample establishments for the ECI, as well as the

EBS, will now be a subsample of the parent NCS establishment sample, which is drawn

from a probability sample of metropolitan statistical areas and non-metropolitan counties.

Previously the ECI and EBS sample establishments were selected from all in-scope

establishments in the United States, without geographic clustering. This paper focuses

mainly on the larger parent NCS sample.
2

The original plan called for the entire NCS sample of establishments to be phased

in over a five year period. Then, beginning in the sixth year, a new panel of establishments

would be introduced each year, replacing the establishments that had been in the sample

for five years. However, instead of the original plan, the entire NCS sample is being

initiated at once. Then, over the next five years, one fifth of the sample will be replaced

with a new panel of establishments. This paper will concern itself mainly with the first

group of sample units being initiated.

This paper will describe the sample design and estimation process for the NCS.

Section 2 covers the three stages of sample selection: the area based PSUs, the

establishments, and the occupations. Next, Section 3 will explain the weights associated

with each stage of the sample design. This weighting discussion will include a new weight

at the occupation level which will produce estimates reflecting current employment,

instead of a weight that reflects employment at the time of initiation, like the weight

currently used in the ECI. Finally, there will be a brief discussion in Sections 4 and 5 of

the variance estimator used for the NCS and some issues that are still being researched.

2. Sample Selection

PSU Selection

The design of the NCS involves three stages of sample selection. The primary

sample units (PSUs) are metropolitan statistical areas (MSAs) and non-metropolitan

counties. The NCS sample PSUs are those originally chosen for the most recent design of

the OCS program. The PSUs are broken down into three categories: certainty

metropolitan areas, noncertainty metropolitan areas, and non-metropolitan counties. The

certainty metropolitan areas are the Consolidated Metropolitan Statistical Areas

(CMSAs), areas like New York, NY and Los Angeles, CA, and other MSAs with a total,

non-agricultural employment greater than 560,000. This cutoff was chosen because those

areas with employment totals greater than 560,000 generally have a significant federal

work force and are of primary interest to the President’s Pay Agent. In addition 3 areas,

Raleigh-Durham, NC, Dayton-Springfield, OH, and Huntsville, AL, are certainty

metropolitan areas because of the large federal work force in each of these areas, even

though the total employment in each of these areas is below the 560,000 cutoff. The

remaining metropolitan areas and the non-metropolitan counties were divided into MSA

and non-MSA strata and then partitioned into regions using the Regional Classification of

States from the Bureau of the Census. Within each region and type of area, the areas

were ordered by average annual pay, and then strata were formed with approximately

equal employment. The total sample of areas was allocated across the strata and then one

area was selected in each stratum with pps using 12/92 employment numbers. Overall,

there are 31 certainty met areas, 45 noncertainty met areas, and 70 non-met counties.

Establishment Selection
3

The second stage of sample selection is a set of establishments. An establishment

is typically defined as a single physical location, although it sometimes consists of multiple

locations of a company. An establishment is based on a single Universal Database (UDB)

number. The UDB is compiled from lists of establishments from each state’s

unemployment insurance records. Each UDB number corresponds to a reporting number

from the unemployment insurance database. The establishment, or single UDB number, is

the assigned sample unit. However, government establishments can be defined differently

due to the government clustering process used in creating the universe for the NCS.

Government establishments are defined as a cluster of single physical locations with the

same Employer Identification Number (EIN), which is an identifier on the unemployment

insurance records used for IRS reporting purposes. This is necessary due to the fact that

governments around the country are not consistent in their reporting techniques. The

clustering process allows a uniform establishment definition for government units across

geographical survey areas.

The sample of establishments within a geographical survey area is allocated by

industry. The industry stratification is listed below:

Private Industry: Mining Construction

Manufacturing - Durables Manufacturing - Nondurables

Transportation Communications

Electric, Gas, and Sanitary Services Wholesale Trade

Finance, Insurance, Real Estate Retail Trade

General Merchandise Stores Food Stores

Banking, Savings & Loans Insurance

Services Hospitals

Nursing Homes Rest of Health

Educational Services Higher Education

Elementary and Secondary Educ. Business Services

Local Government: Educational Services Higher Education

Elementary and Secondary Educ. Hospitals

Rest of Health General Administrative

Special Districts

State Government: Education Hospitals

Rest of Health General Government

The industry strata were chosen in this way because of a desire to produce

estimates for each Major Industry Division, like Retail Trade, as well as selected, more
4

narrowly defined industries which are traditionally produced for the ECI, like General

Merchandise Stores and Food Stores.

In certainty areas the total number of establishments to be selected is determined

by local area publication needs. The total number of sample establishments to be selected

for noncertainty areas is allocated among the geographic strata proportional to the

geographic stratum size. In each PSU, the allocation of establishments among the industry

strata is proportional to size, with the constraint of a pre-defined minimum of

establishments in each industry stratum. Allocating the sample proportional to size among

the industry strata results in a lower variance at the aggregate level while instituting a

minimum sample for each stratum lowers the variance at the individual industry breaks.

The minimum sample for the industry strata varies between certainty areas and

noncertainty areas. The minimum is lower for noncertainty areas because the focus is not

on producing estimates for each industry at the PSU level. Instead the noncertainty

allocations are designed to produce estimates at national and regional levels. Within each

industry stratum the designated number of sample establishments are selected pps.

We are considering eventually using a different procedure to allocate the

establishment sample in each industry stratum among noncertainty PSUs. This procedure

is different from both the one described above and from that used in the OCS program,

which is described below. In the OCS program, the total sample allocation among

noncertainty metropolitan PSUs was proportional to the total employment in the sample

PSUs. The disadvantage of that approach is that sample establishments within smaller

sample PSUs tend to have larger weights than sample establishments in larger sample

PSUs of the same assigned employment. This differential weighting for units of the same

measure of size tends to result in larger variances. Allocating proportional to the

employment of the parent geographic stratum is currently being used in NCS. This is an

improvement over the OCS program approach and would work in terms of weight

equalization if there were no industry strata. The existence of industry strata requires

separately allocating the sample in each industry among the noncertainty PSUs. Since we

also have to be concerned with certainty establishments, which among other problems can

cause the sample allocation in some PSUs to be more than the universe size, an approach

will be used that avoids this problem and accomplishes the weight equalization.

The sampling procedure we are considering eventually using is as follows. Each

establishment in the universe that is located in a noncertainty sample PSU is given a

measure of size. The measure of size is the assigned employment of the establishment

divided by the probability of selection of the PSU in which the establishment is located.

For each industry stratum, the establishments are sorted by PSU and a systematic pps

sample of the establishments in that industry is selected. As is customary in this type of

sampling, recursive determination of certainty establishments and recomputation of the

sampling interval are part of the sampling algorithm.

For noncertainty establishments within the same industry stratum, the

establishment weight, with this procedure, is proportional to the assigned employment. In

this discussion, an establishment weight is the reciprocal of the probability of selecting an

establishment in the two stage process of selecting PSUs and then selecting establishments

in the sampled PSUs. (Certainty establishments in this discussion are those establishments,

which conditioned on the set of sample PSUs, are selected with probability equal to one
5

using the sampling approach just described. All other establishments are noncertainty.)

To see that this proportionality holds, let m be the assigned employment of an

establishment in an industry, p be the probability of selection of the establishment’s PSU,

and I be the final sampling interval for the industry. Since the probability of selection of

the PSU is p, while the probability of selection of the establishment, given that the PSU is

selected, is m / (pI), the unconditional probability of selection of the establishment is

m / I . For certainty establishments, the establishment weight is generally not proportional

to the assigned employment. To establish this, consider two such establishments in the

same industry with the same employment m, but one in PSU 1, with a probability of

selection p1

 and the other in PSU 2 with a probability of selection p2. Then the first

establishment would have an establishment weight of 1 1/ p and the second would have an

establishment weight of 1 2/ p . The problem is that, just as the minimum establishment

weight is 1 when establishments are selected from a single stage design regardless of the

establishment employment, the minimum establishment weight is the reciprocal of its PSUs

probability of selection for the two stage sample.

Occupational Selection

The third and final stage of selection is a set of occupations. Within each

establishment, a sample of “defined occupations” is chosen by probability selection of

occupations (PSO). For NCS purposes, a defined occupation is based on the occupation

classification of the Census of Population, further defined by the following

characterizations: full-time or part-time, union or nonunion, time or incentive, and a

determination of the level of work. The occupation’s Level is determined using a generic

leveling process which ranks and compares all occupations selected in an establishment

using the same criteria. This is a departure from the OCS program which used leveling

definitions unique to each occupation. Refer to Cohen (1997) for more information on the

generic leveling process. The number of defined occupations (quotes) sampled within

each establishment is a function of the PSO employment of the establishment. This

employment is determined by creating a list of all occupations in the establishment eligible

for PSO, which at present does not include certain types of occupations, such as those

where the worker sets his or her own pay. At present, the number of quotes selected in

each establishment is determined by using the following chart:

Establishment Employment # of Quotes

1-49 4

50-99 8

100-249 10

250-999 12

1000-2499 16

2500+ 20
6

Once a list of eligible occupations is assembled and the number of quotes needed is

determined, the quotes are selected by an equal probability systematic sample of individual

employees in the establishment. This is equivalent to a pps sample of the actual defined

occupations, because once an employee is selected, data is collected for all employees in

that establishment with the same defined occupation.

In some instances, the collected data may not match exactly the assigned

establishment, for example, a company may be unwilling or unable to separate out data for

a single location and instead can only provide data for more than one UDB number.

Weighting adjustments are made to account for any collected data that does not match the

assigned unit. Other complications can arise at the occupation level, such as subsampling

down to a particular department within an establishment. For more details, refer to Black,

Ernst, and Tehonica (1997).

3. Weighting

An independent sample is drawn for each PSU, resulting in separate weighting for

each area sample. The description of the weights in the following section reflect only the

sampling of establishments and occupations for locality estimates. The weights are

multiplied by the reciprocal of the probability of selecting the corresponding PSU for

national, regional, and “Rest of US” estimates. To obtain a final establishment weight, the

assigned sampled establishment weight, which is the reciprocal of the probability of

selecting the establishment, is multiplied by various establishment level adjustment factors.

The adjusted establishment weight is then multiplied by the PSO sampling interval and

occupation level adjustment factors to obtain a final occupation weight. The occupation

weight does not reflect the reciprocal of the probability of selecting the specific

occupation, but must be divided by the total number of employees in the selected

occupation to reflect this value. This results in another final weight known as the

employee or individual weight. It is the employee weight, rather than the occupation

weight, that is used in all NCS estimates. Note that the NCS does not produce any

establishment based estimates, and therefore the final establishment weight is not directly

used in any NCS estimates.

Establishment Weighting

The establishment weighting process begins with the assigned establishment weight

which is simply the reciprocal of the probability of selecting the establishment from the

UDB given that the establishment’s PSU has been selected.

The next stage of the weighting process at the establishment level, is the

establishment nonresponse adjustment which takes a weighting cell approach. All

establishments are put into a defined nonresponse adjustment cell based on assigned

employment, assigned Standard Industrial Classification (SIC) code, and assigned sector,

that is, private industry, local government, or state government. When an establishment is

considered a refusal nonrespondent, its assigned weight multiplied by its employment is

redistributed to the respondents in the same nonresponse adjustment cell. This is done by
7

calculating a nonresponse adjustment factor that is applied to the weight of all responding

establishments.

Let AWki be the assigned establishment weight for establishment i in nonresponse

cell k and let E′ki be the assigned employment for this establishment. The number of

sample units, both respondents and nonrespondents, in nonresponse cell k is denoted as

Tk

 of which the first Rk are the responding units. The nonresponse adjustment factor,

Fk

, that is applied to the responding units in cell k is:

F

AW E

AW E

k

ki ki

i

T

ki ki

i

R

k

k

=

′

′

=

=

∑

∑

1

1

.

Note that this factor redistributes the assigned sample employment and thus preserves the

weighted employment total in each cell which, by the method of sampling used in the

NCS, is always nearly equal to the frame employment total for that cell. If instead of

computing Fk

 as we have done, we had omitted E′

ki in the above formula, we would have

preserved the weighted total number of establishments in each cell rather than the

weighted employment total. Since all NCS estimates are employee based it is more

important to preserve employment totals.

There are additional establishment level adjustments that occur when the collected

unit differs from the originally assigned unit. The different types of situations where such

adjustments are needed are listed below.

Subsampling of physical locations: If an assigned unit consists of multiple

locations, it may be necessary, for reasons of interview burden, to collect data only

from a sample of the physical locations, requiring an adjustment factor ( SAFki ) to

reflect this extra stage of sampling.

Merges: If a company provides data for all locations in an area and two or more

of these locations are assigned units then an additional adjustment factor ( MAFki )

is needed for what we call a merge situation This factor is applied separately to

the weight of each of these locations and the factor is generally different for

different locations of the same company.

Collected less than assigned: If an assigned unit consists of multiple locations

then, due to nonresponse, data may not be collected for all locations, even when

no subsampling is used. For example, at the time of data collection, some of the

locations may be owned by another company which will not provide the data. A

special type of nonresponse adjustment is needed in this situation.

Collected more than assigned: If an assigned unit consists of a single physical

location for a company, but the company is only willing to provide data for all its
8

locations in an area, an adjustment is needed to compensate for this additional

data.

The adjustments for collected less and more than assigned are known as the

documentation adjustment factor ( DAFki ).

These adjustments and how they are computed, are described in detail in Black,

Ernst, and Tehonica (1997).

The final establishment weight, FWki , is computed by multiplying the assigned

sampled establishment weight AW

ki

, which is the reciprocal of the probability of selecting

the establishment, by the adjustment factors for nonresponse, documentation, and merges.

FWki = AWki × Fk × DAFki × MAFki × SAFki

An additional weighting step is needed in a merge situation. The final weights

computed as above for each assigned unit that is part of the merge are summed to obtain a

combined weight that applies to the combined data of all locations that the company is

reporting for. This step is described in detail in Black, Ernst, and Tehonica (1997).

Occupational Weighting

For occupational weighting for occupation j in establishment i, we begin with the

final establishment weight for establishment i, which we now denote as FWi, dropping the

subscript for the establishment nonresponse adjustment cell. We multiply this weight by

the PSO sampling interval, I

i

, used in the occupational selections for establishment i. The

PSO sampling interval is the number of PSO employees in the establishment divided by the

number of occupational selections. This is then multiplied by the duplication or collapsing

factor, Cij , that designates how many times occupation j in establishment i was selected

during PSO. The product of these three terms is referred to as the occupation weight

before occupational nonresponse adjustment, which we denote by OWij .

OWij = FWiIi Cij ⋅ ⋅

Note that I

i

is not the reciprocal of the probability of selecting occupation j in

establishment i given that that the establishment has been selected. To obtain that quantity

we would have to divide I

i

 by the number of employees in establishment i that have

occupation j. This will be discussed later in more detail.

Two tiers of occupational nonresponse adjustments are applied to the occupation

weight, OWij , to obtain the final occupation weight. The first is associated with the Level

of the occupation, the second with the Major Occupational Group (MOG) of the

occupation. Each occupation sampled is coded into a MOG and is leveled using generic

leveling criteria as described earlier. The nonresponse adjustment cells for the Level tier
9

of adjustment are defined by the establishment’s reported industry, reported employment

size, and the occupation’s MOG and Level. For the MOG tier of adjustment, the

adjustment cells are defined by the establishment’s reported industry, reported

employment size, and the occupation’s MOG.

The need for these two tiers of adjustment is explained below. During collection

there are times when the field economist will not be able to obtain the desired collection

data for a particular sampled occupation from a respondent establishment. When this

happens, the occupation is deemed a nonrespondent and an adjustment is made. Two of

the four variables that are used in forming the nonresponse adjustment cells, reported

industry and reported employment, are always known for a respondent establishment with

a nonrespondent for a particular occupation. A requirement for data collection is that a

third variable, MOG, be obtained for all occupations, including nonrespondent

occupations. The fourth variable, the Level of the occupation, may or may not be

obtained for a nonrespondent occupation. If the Level information is obtained, the

nonrespondent occupation is assigned to the adjustment cell for the Level tier of

adjustment to which it belongs. If the Level information cannot be obtained, the

occupation is not used during the Level tier of weight adjustment. It is assigned to the

appropriate adjustment cell for the MOG tier of adjustment.

It is worth noting that there are occupations that are not able to be leveled. These

are occupations such as artists, dancers, and actors to which a Level is not appropriate.

Occupations of this type are put into a separate “Level” of their own and are used during

the Level tier of adjustment.

The level tier of adjustment is performed first. The adjustment factor for the k-th

nonresponse cell for the level tier is denoted as FLk and computed as follows. Let RLk

be the set of respondent occupations in this cell and let TLk be the set of all occupations,

respondents plus Level tier nonrespondents in the cell. Then:

F

OW

OW Lk

ij

ij T

ij

ij R

Lk

Lk

=

∈

∈

∑

∑

The occupational weight for occupation ij, after the Level tier nonresponse adjustment, is

denoted as LOWij . For a respondent occupation it is OWij FLkij

⋅ where kij is the level

tier nonresponse adjustment cell for occupation ij. For a MOG tier nonrespondent

occupation, LOWij = OWij , that is no adjustment is applied to these nonrespondents.

The MOG tier of adjustment is then performed. It is computed similarly to the

Level tier adjustment with LOWij replacing OWij . The adjustment factor for the k-th

nonresponse cell for the MOG tier is denoted as FMk . Now RMk is the set of respondent

occupations in this cell and TMk is the set of all occupations, respondents plus MOG tier

nonrespondents, in the cell. Then
10

F

LOW

LOW Mk

ij

ij T

ij

ij R

Mk

Mk

=

∈

∈

∑

∑

.

The final occupational weight for respondent occupation ij, which is obtained after

the MOG tier nonresponse adjustment factor is applied, is denoted as FOWij . It is simply

LOWij FMkij

⋅ where kij is the MOG tier nonresponse adjustment cell for occupation ij.

Thus the final occupation weight for occupation ij, is the final establishment weight

for establishment i multiplied by the PSO sampling interval, the duplication factor, and

the two occupation nonresponse adjustment factors.

One more weight is needed. The final employee or individual weight, is denoted

by EWij , for occupation j in establishment i. EWij is simply defined as

EWij = FOWij NFij /

where NFij is the number of PSO employees in occupation ij at the time of the first or

initiation interview. It is EWij rather than FOWij that is used to obtain all NCS estimates.

Much of the weighting for the NCS reflects the approach used in the ECI, since

the ECI concept of first sampling establishments and then sampling defined occupations

from an establishment list of employees or occupations for each sample establishment has

been carried over to the NCS. One major difference is that the ECI has been using a

weight essentially equivalent to the occupation weight defined above, which also is known

as the “quote weight,” to compute estimates of mean wages. NCS is using the employee

weight defined above, for estimates of mean wages, total number of workers in a domain

and for positional statistics, such as medians. Generally these two weighting approaches

yield precisely the same estimates of mean wages and total workers at the time of the

initiation interview. However, for update interviews, estimates of means produced using

quote weights, unlike those produced using employee weights, only reflect the

employment at the time of the initiation interview, not current employment. Furthermore,

quote weights are completely inappropriate for computing positional statistics. We first

proceed to describe these two approaches to weighting. The description focuses only on

the components of these weights arising from the probability selection of the establishment

and occupations. All the components relating to nonresponse and other adjustments are

ignored. Therefore, we will obtain a somewhat simplified version of the occupation

weight and employee weight that we have previously defined and will use a different

notation.

For the i-th establishment let: pi

 be the probability of selection of the

establishment; I

i

 be the sampling interval used in the occupational selections, that is the

number of employees on the sampling list divided by the number of occupational

selections; and NFij NCij , denote the number of employees in occupation j in this

establishment at the time of the first, or initiation interview, and at the time of the current
11

interview, respectively. Then the quote weight wQij for occupation ij is I p i i

/ if both the

establishment and occupation are selected; otherwise wQij = 0 . Similarly the employee

weight wEij for occupation ij is Ii NFij pi/ ( ) if both the establishment and occupation

are selected; otherwise wEij = 0 .

An estimate , for example, of total wages in a domain using employee weights is

obtained by multiplying the employee weight for each current employee within the domain

by the employee’s wages and summing the product over all sampled employees within the

domain. An estimate of total employment within a domain is obtained by summing the

employee weights over all current sample employees within the domain. An estimate of

mean wages using employee weights is obtained by taking the quotient of the previous

two estimates.

An estimate of total wages in a domain using quote weights is obtained by

multiplying the quote weight for each quote within the domain by the mean wages for the

quote and summing the product over all quotes within the domain. An estimate of total

employment in a domain using quote weights is obtained by summing the quote weights

over all quotes within the domain. An estimate of mean wages using quote weights is

obtained by taking the quotient of the previous two estimates.

To establish that employee weights yield unbiased linear estimators of the data,

such as total employees or total wages in a domain, while the quotes weights do not, we

first observe that since pi

 is the probability of selection of the establishment and N I Fij i/

is the expected number of times that occupation ij is selected given establishment i is

selected, it follows that

E wEij ( ) = 1 (1)

From (1) and the definitions of the employee and quote weights it follows that

E wQij NFij ( ) = . (2)

If YCijk is the value of the characteristic of interest for the employee k in occupation ij at

the current time, YCij YCijk NCij

k

= ∑ / . IfYC is the population total and $,

$ YEC YQC are the

employee weight and quote weight estimators of YC, respectively, then by (1) and (2),

E YEC E wEij YCijk YCijk YC NCijYCij

ijk ijk ij

(

$

) = ∑ ( ) = ∑ = = ∑ , (3)

E YQC E wQij YCij N Y Y

ij

Fij Cij

ij

C

(

$

) = ∑ ( ) = ∑ ≠ . (4)
12

While by (3), $YEC is an unbiased estimator of YC, 

$YQC is a biased estimator by (4) that

weights the mean YijC by the employment NFij at the time of the initiation interview

rather than the current NCij .

Note that in the special case when the characteristic of interest is total employment

in a domain D, then the characteristic value for each employee associated with a quote is 1

and therefore YCij = 1 for all ij. Then, by (3), (4), E YEC NCij

i j D

(

$

)

,

=

∈

∑ ,

E YQC NFij

i j D

(

$

)

,

=

∈

∑ and thus $YEC ,

$YQC estimate total employment in D at the time of the

current interview and the initiation interview, respectively.

The observation in the previous paragraph, that the sum of the quote weights in a

domain estimates the employment in the domain at the time of initiation, explains the one

use of quote weights in the occupational weighting process, namely in the occupational

nonresponse adjustment. The occupational nonresponse adjustment redistributes the

occupation weights of the nonrespondents in a cell at initiation to the respondents and

therefore preserves the estimated employment in the cell. Employee weights could not be

used for this purpose, because employee weights depend on the number of employees in

an occupation, which is not known for nonrespondent occupations. This explains why the

employee weight is not defined until the last step of the entire weighting process,

subsequent to the occupational nonresponse adjustments.

4 Variance Estimation

The following is a very brief overview of the variance estimator. A complete

description is provided in Tehonica, Ernst, and Ponikowski (1997). The variance

estimation formulas for mean wages are obtained using a linearized Taylor Series form.

For noncertainty establishments, the overall variance is estimated by a pps with

replacement formula reflecting the fact that the first stage of sampling in a PSU is a pps

sample of establishments. The component of variance for the certainty establishments is

estimated by a simple random sample without replacement formula, reflecting the fact that

the sample of occupations within an establishment is typically obtained through a

systematic sample of employees and for certainty establishments the first stage of sampling

in a PSU is the sample of occupations.

5 Future Research.

The following is a list of some NCS sample design research topics that we would

like to investigate:

Determine the effect on variances of alternatives to the current allocation

of the number of occupational selections per sample establishment by size class.

This work can either focus on reducing variances with the same total respondent
13

burden or reducing respondent burden while minimizing the negative impact on

variances. This work has already begun and is detailed in Paben and Ernst (1997).

Estimate the between establishment and within establishment components

of variances. This would allow us to determine if we need to make any changes in

our allocation of resources between the number of sample establishments and the

number of sample occupations per establishment

Determine an optimal allocation of the sample establishments among the

industry strata. Allocating an equal number of establishments per stratum is

generally effective for separate estimates by industry stratum, while allocating

proportional to stratum size tends to produce lower variance for overall estimates

that cut across strata. We need a compromise solution. An analogous problem

exists for allocating among geographic PSUs, where an equal number of

establishments per PSU is typically appropriate for local area estimates if we wish

to be able to publish the same amount of data for each PSU, but yields a poor

allocation for national and regional estimates.

Modification of the sample design to reduce the “birth occupation” bias. A

panel will remain in sample for five years. Any establishments that begin business

during this period will not be represented in the panel. This problem can be

overcome by means of an additional “birth sample” of such establishments, as

described in Black, Ernst, and Tehonica (1997). There is also the related problem

of birth occupations. These are occupations that were not present in a sample

establishment at the time of the initiation interview, but are currently present.

Since, under our present procedures, we only select occupations at the initiation

interview, such occupations would not be represented in our sample. It would

appear that overcoming this problem would require an additional occupational

selection procedure analogous to a birth sample of establishments, which would

involve additional respondent and interviewer burden.

Allocation of the sample establishments to collection panels to allow for an

equal spread of sample units over all geographic areas for the entire calendar year.

Although no final decisions have been made, it is envisioned that data collection

will be year around in the larger areas with quarterly collections panel used to

insure that the sample is spread evenly over the four quarters. In smaller areas

data collection will take place in only one quarter with the collection quarter

chosen on a probability basis. Such an approach will facilitate interarea

comparisons and are necessary to produce local wage indexes as proposed for the

larger metropolitan areas.

Usage of the Occupational Employment Statistics (OES) survey in NCS

design and estimation. OES is another BLS survey program, which produces

occupational data and which has a much larger sample size than NCS. It is

possible to use OES in several ways. We could use OES to benchmark our

estimates on a MOG basis. We could also use OES as a sampling frame for the

NCS sample. This would allow us, for example, to oversample OES sample

establishments that are rich in occupations that we typically obtain in insufficient

quantity, such as high paying occupations.
14

Development of a method to designate the ECI and EBS samples as a

subsample of the larger NCS sample. This may appear straightforward at first, but

there are some complications. For example, since the NCS is a pps sample in each

industry stratum × PSU, an equal probability subsample would produce an ECI

and EBS sample that is pps. However if we take an equal probability subsample of

all establishments, including certainty establishments, then even some of the largest

NCS certainty establishments may not be selected into the ECI and EBS samples,

which would have negative consequences on variances. We have developed a

sampling strategy to overcome this problem.

References

Black, S. R., Ernst, L. R., and Tehonica, J. (1997), “Statistical Issues Associated with

Data Collection for the National Compensation Survey,” in Proceedings of the Survey

Research Methods Section, American Statistical Association, to appear.

Cohen, S. H. (1997), “The National Compensation Survey: The New BLS Integrated

Proceedings of the Survey Research Methods Section,

American Statistical Association, to appear.

Paben, S. P., and Ernst, L. R. (1997), “Adjusting the Number of Occupational Selections

for the National Compensation Survey.” in Proceedings of the Survey Research Methods

Section, American Statistical Association, to appear.

Tehonica, J., Ernst, L. R., and Ponikowski, C. H. (1997), “Summary of Estimation and

Variance Specifications for the 1996 Albuquerque, NM COMP2000 Test Survey,”

Bureau of Labor Statistics memorandum to The Record, dated March 3.

Any opinions expressed in this paper are those of the authors and do not constitute policy

of the Bureau of Labor Statistics.