Scientists,

Engineers, and

Technicians in

Trade and Regulated

Industries: 1994

Detailed Statistical Tables

Division of Science Resources Studies

Directorate for Social, Behavioral and Economic Sciences

National Science Foundation NSF 98-305
Scientists,

Engineers, and

Technicians in

Trade and Regulated

Industries: 1994

Detailed Statistical Tables

Richard E. Morrison, Project Officer

Division of Science Resources Studies

Directorate for Social, Behavioral and Economic Sciences

National Science Foundation NSF 98-305
ii

Suggested Citation

National Science Foundation, Division of Science Resources Studies, Scientists,

Engineers, and Technicians in Trade and Regulated Industries: 1994, Detailed

Statistical Tables, NSF 98-305, by Richard E. Morrison (Arlington, VA, 1998).

Availability of Publications

Single copies are available free of charge from the Division of Science Resources

Studies, National Science Foundation, Arlington, VA 22230. SRS data are also

available through the World Wide Web (http://www.nsf.gov/sbe/srs/stats.htm). If you

are a user of electronic mail and have access to either Internet, you may order

publications electronically. Send requests to pubs@nsf.gov. In your request, include the

NSF publication number and title, number of copies, your name, and a complete mailing

address. Printed publications may also be ordered by telephone 301-947-2722. Please

allow 4 weeks for delivery.

Telephonic Device for the Deaf

(703) 306-0090
iii

ACKNOWLEDGMENTS

 The development of Scientists, Engineers, and Technicians in Trade and

Regulated Industries: 1994 was managed by Richard E. Morrison, Senior

Economist, National Science Foundation, Division of Science Resources Studies

(SRS), Human Resources Statistics Program (HRS), under the direction of Mary J.

Golladay, Program Director, HRS, and Carlos E. Kruytbosch, Program Director of

the former Science and Engineering Personnel Program. Overall direction and

guidance were provided by Jeanne E. Griffith, Director, SRS. Anne M. Houghton,

Julia H. Harriston, and Tanya Gore of the Publications Management Group of

SRS provided copyediting, processing, and final composition for this report. 

Mathematica Policy Research, Inc. (MPR), under NSF contract number SRS-88-

21853, prepared the tables from data collected, assembled, and tabulated by the

Bureau of Labor Statistics (BLS), U.S. Department of Labor. MPR staff members

who worked on this project were Kurt Greenstone, Ben Liu, Sharon Maccini, and

Sue Willet. BLS staff who contributed to this project included Michael P. McElroy,

Lawrence Jeff Johnson, and Bruce R. Montgomery.
CONTENTS

Section Page

GENERAL NOTES ............................................................. 1

TECHNICAL NOTES ........................................................... 3

GENERAL ........................................................................ 3

Scope of Survey .............................................................................................................. 3

Method of Collection ...................................................................................................... 4

Sampling Procedures ...................................................................................................... 4

Response ......................................................................................................................... 5

Occupational Estimates .................................................................................................. 6

Variance Estimates ......................................................................................................... 7

Reliability of Estimates ................................................................................................... 8

 Quality Control Measures .............................................................................................10

LIST OF TABLES............................................................... 11

DETAILED STATISTICAL TABLES ........................................... 13

GETTING INFORMATION ON THE WORLD WIDE WEB

ORDER FORM

v
1

GENERAL NOTES

In this report, estimates are presented of the total number of positions filled by scientists,

engineers, and technicians employed in trade and regulated industries in 1994. The estimates were

developed from the Occupational Employment Statistics (OES) Survey, a Federal/state program

under which national and state estimates are generated of employment by industry for nonfarm

wage and salary workers. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor

has primary responsibility for developing OES survey procedures and for providing states with

technical guidance and assistance with survey problems. State Employment Security Agencies

implement the survey at the state level and prepare current and projected employment statistics for

these labor markets. Some states also prepare substate estimates.

The National Science Foundation has enhanced the BLS effort since 1977 by financing the

collection of detailed estimates on the types of scientific and technical jobs filled by industry. 

Analysis of this information yields insight into the dynamics of the labor market.

Industries identified in the tables of this report are from the "Numerical List of Short Titles"

in the Standard Industrial Classification (SIC) Manual, revised edition. The numbers of scientists,

engineers, and technicians for a few industries at the 2-digit SIC level (tables A-2 and A-4) in this

1994 edition, as well as in the 1991 edition, differ from those in the prior (1988) report on trade and

regulated industries, because the industries were recoded between the 1986 and 1989 surveys. 

Starting with the 1991 edition, greater noncomparability occurred at the 3-digit level of detail

(tables A-1, A-3, A-4, and A-5) because of more extensive recoding. The numerous industry title

changes did not affect the comparability of the 1991 and 1988 occupational estimates.

For the reasons outlined above, estimates in the tables of this 1994 report and the 1991

report should be compared with those for 1988 (and earlier years) only after consulting the 1987

SIC revisions to determine industry comparability.

Requests for previously published data and additional information should be directed to

Richard E. Morrison

Senior Economist

National Science Foundation

4201 Wilson Boulevard, Suite 965

Arlington, VA 22230

Tel: (703) 306-1780 ext. 6904

Fax: (703) 306-0510

Internet: rmorriso@nsf.gov
3

 

TECHNICAL NOTES1

General

National estimates of occupational employment in the scientific, technical, and engineering fields

for trade and regulated industries2 were based on data from the 1994 Occupational Employment

Statistics (OES) Survey. The OES Program is a Federal-state cooperative effort in which each

state conducts its own survey to produce its estimates. The Bureau of Labor Statistics (BLS)

provided each state with survey procedures, technical support, and troubleshooting assistance.

The government agencies participating in this program were the 50 State Employment Security

Agencies (SESAs) plus the District of Columbia, Puerto Rico, Guam, and American Samoa. For

this report, estimates at the national level were produced by BLS-Washington based on data from

the fifty states plus the District of Columbia. State-level estimates can be obtained from the

individual SESAs.

Scope of Survey

The BLS trade and regulated industries survey covers establishments in SIC codes 40-42, 44-59,

and 823. [SIC 823 —“Libraries” —is not reported herein.] The reference dates of this survey

were the weeks that included April 12, May 12, or June 12, 1994. The reference date for any

particular unit in the survey depended on its SIC code. See the chart below for those SICs

covered in this publication..

SIC CODE REFERENCE DATE

 40 May 12

 41 May 12

 42 May 12

 44 Apr 12

 45 June 12

 

1 Portions of the material in these Technical Notes have been excerpted or reproduced from U.S. Department of

Labor, Bureau of Labor Statistics: Occupational Employment Statistics, 1994 (Bulletin 2468, March 1996,

“Appendix A. Survey Methods and Reliability of Estimates,” pp. 94-98).

2 A note on industrial classification nomenclature: Industries denoted as “Trade and Regulated Industries” herein are

referred to as “Nonmanufacturing Industries” in BLS publications. [See “Scope of Survey” below for listing of

industries covered.] In NSF publications “Nonmanufacturing Industries” refer to SIC codes 10, 12-17, 60-65, 67,

70, 72, 73, 75, 76, 78-80 (except 806), 81, 83, 84, 86, 87, and 89. In BLS publications the industries listed above

are called “Mining, Construction, Finance, and Services Industries” and are covered by the same survey cycle that

NSF reports as “Nonmanufacturing Industries.” In the NSF data brief announcing this publication (“Services

Sector S&E Employment Rises, Then Falls Sharply As Engineering and Technician Jobs Are Cut” —NSF 97-322)

industries referred to herein as “Trade and Regulated Industries” are termed “Services Industries” to more clearly

connote to readers the industries they encompass.
4

SIC CODE REFERENCE DATE

 

 46 June 12

 47 Apr 12

 48 May 12

 49 Apr 12

 50 June 12

 51 June 12

 52 June 12

 53 June 12

 54 June 12

 55 June 12

 56 June 12

 57 June 12

 58 June 12

 59 June 12

 

Method of Collection

Survey schedules were initially mailed out to the personnel offices of almost all sampled

establishments. Some of the larger establishments, however, received a personal visit.

Two additional mailings were sent to nonrespondents at approximately six-week intervals.

Nonrespondents that were critical to the survey because of their size received a telephone call or

a personal visit followup.

Sampling Procedures

The sampling frame for the OES survey was a list of units reported to the state's Unemployment

Insurance (U.I.) files. The reference date of the sampling frame was the second quarter of 1993.

Within each state, the universe was stratified by SIC and size class where size class was defined

as follows:

 Size Class Employees

1 1-4

2 5-9

3 10-19

4 20-49

5 50-99

6 100-249

7 250-499

8 500-999

9 1000+
5

U.I. reporting units with fewer than 5 employees were not sampled in most states; instead, units

with 5-9 employees were assigned a larger weight to account for employment in size class 1. U.I.

reporting units with 250 or more employees were included in the sample with certainty. The

sample sizes needed to calculate state estimates at a targeted relative standard error of 10, 15, or

20 percent for one standard deviation were developed for each SIC across its noncertainty size

classes. The sample size for each SIC was determined by calculating averages of occupational

rates and averages of coefficients of variation (CVs) for a given set of typical occupations using

data from the previous survey round. Within each SIC, the sample size was then allocated

proportionally across size classes based on size class employment. The sample was selected

systematically with equal probability within each state/(area)3/SIC/size class cell.

The states were given the option of selecting three target relative standard errors in designing

their samples. Many states took advantage of this flexibility by varying target relative standard

error across SICs in order to balance the cost and reliability of their estimates.

The above allocation resulted in a total initial sample size of 282,861 U.I. reporting units

nationally.

Response

Of those sampled, 269,986 were eligible units (i.e., respondents, refusals, unusables, and

nonrespondents). Usable responses were obtained from 205,203 units, producing a response rate

of 76.0 percent based on units and 73.9 percent based on weighted employment.4 See the table

below for additional details.

Unit Type Unit Count Weighted Empl. Count

Eligible 269,986 46,214,225

Respondents 205,203 34,142,013

Nonrespondents 64,783 12,072,212

Out-of-scope and 12,875

Out-of-business

Initial sample size 282,861

 

3 Some states opted to further stratify their samples by substate areas.

4 Subsequent to the closeout date for national estimates, additional data were collected by the states and used to

prepare their respective estimates. Consequently, the response rates in most states were higher than the response

rate used to develop national estimates. 
6

Occupational Estimates

Weights were determined for sample units that had usable response. Each weight was composed

of two factors, the reciprocal of the probability of selection and a nonresponse adjustment factor

(NRAF).

For questionnaires that were not returned or were otherwise unusable, an NRAF was calculated

to impute for the missing data. This factor was the ratio

 Weighted sample employment of all eligible units in sample

 Weighted sample employment of all usable eligible units .

It was calculated for each state/three-digit SIC/size class sampling cell.

The sample employment used to calculate the NRAF was obtained from the sampling frame. If

the NRAF in a cell was greater than a predetermined maximum factor (the latter increases as the

number of respondents in a cell increases), the cell was collapsed with other homogeneous cells

in the industry until the NRAF for the combined cell was not greater than the appropriate

maximum factor. If the collapsing procedure terminated (i.e., no more cells were available for

collapse) before satisfying the constraint above, then the most recent maximum factor was used.

Note that homogeneous cells were adjacent size cells within a state and SIC. The final weight

assigned to each usable unit in the sample was the product of the NRAF and the reciprocal of the

probability of selection.

A separate ratio estimate of occupational employment was used to develop national estimates.

The auxiliary variable used was the 1992 population value of total employment. This variable is

also referred to as cell benchmark employment, denoted by Mij. The term

 Mij

Σ Wijk Eijk

k

is known as the benchmark factor. It is the ratio of cell benchmark employment to cell weighted

reported total employment. The estimation formula below produced final estimates (Pij) of

occupational employment through benchmarking, that is, the process of multiplying the cell's

weighted reported occupational employment (ΣWijk Pijk) by its benchmark factor.

 ^ Mij

Pij = (Σ Wijk Pijk) ( )

k Σ Wijk Eijk

 ^

where Pij = estimated employment for occupation P in industry i and size class j

i = a three-digit industry

j = size class
7

k = establishment

Wijk = weight for establishment k in industry i and size class j after adjusting for

 nonresponse

Pijk = reported employment for occupation P in establishment k within industry i and

 size class j

Eijk = reported total employment for establishment k in industry i and size

 class j

Mij = population value of total employment for industry i and size class j.

The estimated employment for an occupation at the three-digit industry i level was obtained by

summing the occupational employment estimates Pij across all size levels j within industry i.

^

 Li ^

Pi = Σ Pij

 j=1

where Li was the number of size levels j in industry i.

Similarly, the estimated employment for an occupation at the two-digit industry g level was

obtained by summing the occupational employment estimates Pi across all three-digit industries i

within two-digit industry g.

 ^ Lg ^

Pg = Σ Pi

 i=1

where Lg was the number of three-digit industries i in industry g.

It is important to note, however, that because of publishability requirements, rounding

adjustments were made such that occupational employment estimates at the three-digit industry

level may not sum to the two-digit level estimates.

Variance Estimates

Estimates of sampling error were calculated on survey estimates to allow users to determine

whether or not the occupational estimates were reliable enough for their needs. Only a

probability sample can be used to estimate sampling error from a sample.

The formulas used to estimate the variance, a common measure of sampling error, were based on

the sample design and on the method of estimation. In the OES survey, the formula used to

estimate the variance of occupational employment was a subsample replication technique called

the jackknife random group. The jackknife derives R estimates of total occupational

employment from R subsamples of the parent sample by excluding one random group at a time.

The jackknife then estimates the variance of the parent sample's employment estimator from the

variability between the R employment estimates.
8

The variance for an occupational employment estimate at the three-digit industry i/size class j

level is

 ^ 1 R ^ ^

 S2(Pij) = Σ (Pijr - Pij)2

 (R)(R-1) r=1

Where

 ^ ^

S2 (Pij) = estimated variance of Pij

R = number of random groups

 ^

Pij = estimated employment for occupation P in industry i and size class j

 ^

Pijr = estimated employment for occupation P in industry i, size class j, and

 and subsample r

 ^

Pij = estimated mean employment for occupation P in industry i and size class j

 across R subsamples

The above formula for variance has been simplified. The actual formula includes corrections for

finite populations.

The variance for an occupational employment estimate at the three-digit industry i level is

obtained by summing the variances S2 (Pij) across all size levels j within industry i.

 ^ Li ^

S2 (Pi) = Σ S2 (Pij)

 j=1

where Li is the number of size levels j in industry i.

Similarly, the variance for an occupational employment estimate at the two-digit industry g level

is obtained by summing the variances S2(Pi) across all three-digit industries i within industry g.

 ^ Lg ^

S2 (Pg) = Σ S2 (Pi)

 i=1

where Lg is the number of three-digit industries i in industry g.

Reliability of Estimates

Estimates developed from the sample may differ from the results of a complete census of all the

establishments in the sampling frame. Two types of error, sampling and nonsampling, are

possible in an estimate based on a sample survey. Sampling error occurs because observations

are made only on a sample, not on the entire population. Nonsampling error can be attributed to

many sources, e.g., an inability to obtain information about all cases in the sample, differences in

the respondents' interpretation of questions, inability or unwillingness of respondents to provide

correct information, errors made in recording, coding, or processing the data, errors made in

estimating values for missing data, and failure to represent all units in the population.
9

Sampling error arises because the particular sample used in this survey is only one of a large

number of possible samples of the same size that could have been selected with the same sample

design. Estimates derived from those different samples would differ simply as a result of random

effects. Relative standard standard errors that are a measure of that sampling error effect are

presented in this publication. The relative standard errors of a survey estimate measure the

variation among the estimates from all possible samples. The relative standard error is the

standard error of the estimate divided by the employment estimate for that occupation. Thus, it

shows the size of the standard error relative to the occupational estimate itself.

Use of the relative standard error enables the analyst to construct a confidence interval around the

occupational estimate. The confidence interval includes the average value of the estimates

obtained from all possible samples (of that size and design) at a confidence level specified by the

analyst. If no nonsampling error is present (which is unlikely) the a priori interval will contain

the true value with the confidence level specified.

To construct the confidence interval, divide the relative error shown in the table by 100 and

multiply the result by the occupational estimate. The confidence interval is the occupational

estimate, plus or minus the number resulting from the calculation described above. This estimate

yields a confidence level of approximately 68 percent. That is, in 68 percent of the possible

samples, the "true value" (neglecting nonsampling error) will be contained in the interval. Most

analysts prefer to have a confidence level higher than 68 percent. If a 90 percent confidence level

is desired, multiply the number produced from the calculation in the first sentence above by 1.6.

For a 95% confidence level, multiply by 1.96. For almost full confidence (99 percent), multiply

by 2.57.

For example, suppose the occupational employment estimate for chemist is 5,000 with an

associated relative standard error shown on the table of three. The 68 percent confidence

interval will then be (3/100) x 5,000 or the chemist estimate plus or minus 150. The "true value"

will be contained in the interval of 4,850 to 5,150 about 68 percent of the time. For 95 percent

confidence, multiply 150 times 1.6 = 240. The 95 percent confidence interval is 4,760 to 5,240.

It is important to remember that nonsampling error can have important effects on the accuracy of

the estimate. Unfortunately nonsampling errors can be very difficult to measure and are not

available.

The relative standard errors primarily indicate the magnitude of the sampling error. They do not

measure nonsampling error, including any biases in the data. Many edit and quality control

procedures are used to reduce the nonsampling error caused by mistakes in recording, coding,

and processing the data. The adjustments made for nonrespondents assumed that the

characteristics of the nonrespondents are the same as those of the respondents at a given level.

To the extent that this is not true, bias is introduced in the data. The magnitude of this bias is not

known.

Particular care should be exercised in the interpretation of small estimates or small differences

between estimates, because of relatively large sampling errors and the unknown magnitude of the

biases.
10

Quality Control Measures

As described above, the OES Survey is a Federal-state cooperative effort in which states conduct

their own surveys.

A major concern with a cooperative program like OES is accommodating state-specific

publication needs with limited resources while standardizing survey procedures across all fifty

states and the District of Columbia in order to produce quality estimates. The control on sources

of nonsampling error in this decentralized environment can be particularly difficult. In addition,

the review and validation function is spread across eight regional offices, thus leading to

procedural differences between regions. Examples of quality control measures employed by BLS

are the Survey Processing and Management System (SPAM) and the Estimates Delivery System

(EDS), which were developed to provide a consistent and automated framework for state

procedures and to reduce the workload at state, regional, and national levels.

By standardizing data processing activities (i.e., validating the sample frame, allocating and

selecting the sample, refining mailing addresses, addressing envelopes and mailers, editing and

updating questionnaires, producing management reports, and producing estimates) across all

states, the use of SPAM and EDS has also standardized the survey methodology. This has

significantly reduced the number of errors on the data files as well as the time needed to review

them.

Other quality control measures used in the OES survey include

- followup solicitations of nonrespondents (especially essential nonrespondents),

 - review of schedules to verify the accuracy and reasonableness of the reported data,

 - adjustments of atypical data reporters,

 - validation of the nonresponse adjustment factors,

 - validation of the benchmark employment figures and of the benchmark factors, and

 - validation of the analytical tables of estimates (at the two and three-digit SIC levels).
LIST OF TABLES

Table Page

A-1 Scientific, engineering, and technical (SET) personnel and SET

managers in trade and regulated industries, by detailed industry of

employment: 1994 .............................................................................................. 13

A-2 Scientists in trade and regulated industries, by broad industry group of

employment and detailed occupation: 1994 ......................................................... 17

A-3 Engineers in trade and regulated industries, by broad industry group of

employment and detailed occupation: 1994 ......................................................... 18

A-4 Technicians in trade and regulated industries, by broad industry group of

 employment and detailed occupation: 1994 ........................................................ 19

A-5 Scientists, engineers, and technicians in trade and regulated industries as

a percentage of total industry employment, and the relative standard

error: 1994......................................................................................................... 20

A-6 R&D scientific, engineering, and technical (SET) personnel and SET

managers in trade and regulated industries, by detailed industry of

employment: 1994 .............................................................................................. 47

A-7 R&D scientists in trade and regulated industries, by broad industry group

of employment and detailed occupation: 1994 ..................................................... 48

A-8 R&D engineers in trade and regulated industries, by broad industry group

of employment and detailed occupation: 1994 ..................................................... 49

A-9 R&D technicians in trade and regulated industries, by broad industry

group of employment and detailed occupation: 1994 ........................................... 50

A-10 R&D scientists, engineers, and technicians in trade and regulated

industries as a percentage of total industry employment, and the relative

standard error: 1994 ......................................................................................... 51

11