Draft-- 6/2/97 

Sample Redesign for the Current Employment Statistics Survey 

Shail Butani, George Stamas, Michael Brick 

Key Words: Multi-establishment firms; permanent random numbers; and atypical units. 

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

of the Bureau of Labor Statistics. 

I. Introduction 

In this paper, we present some issues as well as empirical results pertaining to the sample 

redesign for the Bureau of Labor Statistics’ (BLS) Current Employment Statistics (CES) 

Survey, commonly known as the payroll survey. CES is a survey of 390,000 businesses 

that is conducted monthly in a Federal/State cooperative environment to produce 

estimates of employment, payroll, and hours worked. The month-to-month movements in 

the CES series are closely followed by policy makers and forecasters as timely indicators 

of the overall strength and direction of the nation’s economy. 

We first give the background on the redesign and state the goals. In Section III we give a 

description of the BLS’ Business Establishment List (BEL) that serves as a sampling 

frame for the CES and as a source of “benchmark” or “known population” values for 

employment that lag in time; BEL is based on administrative records. Next, we give a 

profile of multi-site employers as they play an important role during sample allocation, 

selection, and estimation phase. In Section V, we describe the sample design features 

such as sampling unit, stratification, allocation and selection cells, etc. The usage of 

permanent random numbers for frame maintenance, updating probabilities of selection, 

inclusion of new businesses (births) and removal of out-of-business units (deaths) and 

sample rotation is given in Section VI. Finally, Section VII covers the transition phase 

followed by a summary section. 

II. Background

The CES is a quota sample whose inception over 50 years ago predates the introduction 

of probability sampling as the internationally recognized standard for sample surveys. 

Under the current design the estimator for employment is: h t

i t im h

i t im h

E h t

emp

emp , E

,

,

, $ $ = ∈

− ∈

−

∑

∑

I

I 1

1 . 

where, m is the set of matched units i that responded in both month t and t-1 and the 

estimate is computed at the estimating cell level h. Estimating cells are usually defined 

by geography (National, State or metropolitan area) and industry. Some estimating cells 

for national estimates are defined by broad size class or geographic region in addition to 

industry. An estimate for a larger domain is the sum of estimates for the component 
 2

estimating cells. With every March estimate, $Eh t, is aligned with the “benchmark” level 

obtained from the BEL. 

This estimator is known as the link relative. It implicitly adjusts for nonresponse and 

nonparticipation by assuming the trend is the same for respondents and nonrespondents. 

Quota samples as well as the link relative estimator are known to be at risk for potentially 

significant biases; these findings for CES were confirmed by recent research. In 

particular, the risk associated with the link relative is that often the units in estimating 

cells are not homogeneous and they are not weighted to compensate for differential 

probabilities of selection. 

This paper describes the research conducted on a proposed probability based sample 

design for the CES. A few of the major goals of the redesign are as follows. 

• The design should produce and maintain over time prespecified levels of reliability at 

the national, state, and Metropolitan Statistical Areas (MSA) levels by various levels 

of Standard Industrial Classification (SIC) code. 

• Users should have the flexibility to produce estimates at any geographic (e.g., county) 

and industry level (e.g., 4-digit SIC) and at any point in time with an estimate of its 

reliability. 

• The total employment estimates (not seasonally adjusted) across various levels of 

geography and industry should be essentially additive. Under the current design, for 

various reasons, there is a wide gap between the sum-of-states and the national 

estimates. 

• The annual revision to “known population values” at the total national level should 

be about two-tenths of a percentage point or less. A revision of one-half of one 

percent on a total nonfarm employment of about 120 million is considered to be very 

large in terms of over-the-year change that is generally of magnitude less than four 

million. 

• The revisions between preliminary and final monthly estimates should be small, i.e. 

less than 50,000. 

• Since CES estimates are typically released about 3 weeks after the reference period it 

should be operationally feasible and relatively simple. 

A major impediment to introducing probability sampling in CES has been issues 

associated with data collection. The Bureau during the last few years has invested a great 

deal of time and effort to improving response rates [Clayton, Werking] and obtaining 

timely reporting. 
 3

III. Business Establishment List

The primary source of BLS’ BEL is the quarterly contributions reports filed by 

employers for each unemployment insurance (U.I.) account with their state 

unemployment insurance agency. The data for both private and public sector workers are 

reported to BLS after they go through several stages of refinement by the employment 

security agencies of the 50 states and the District of Columbia as part of the Covered 

Employment and Wages, or ES-202, Program. Covered employment reported by these 

sources provide a virtual census (98 percent) of employees on nonfarm payrolls. Among 

other data elements, this rich and comprehensive database has about seven million 

records with SIC code, state, county, employment for each month, and total quarterly 

wages mostly broken down at the worksite or establishment level for each U.I. account. 

The quarterly unemployment insurance files are generally transmitted five months after 

the end of the quarter by the states to BLS. It takes BLS an additional 3 months to 

process these files through various edits as well as perform record linkage to previous 

quarters before making it available as the sampling frame. The first quarter file of the 

current year, therefore, is generally available at the end of December or early January of 

next year for sampling and for “benchmarking” purposes. For example, in February of 

1997, BLS can draw a sample from the first quarter 1996 file and at the same time ratio 

adjusts (“benchmark”) to “known population values” the CES sample based estimates for 

the period April 1995 through March 1996; sample based estimates for April 1996 

through January 1997 can then be recomputed using this updated information. At 

present, the benchmarking process is done with the release of May 1997 data in June. 

IV. Characteristics of the Multi-site Employers 

As in most establishment surveys, multi-site employers (i.e., employers that own more 

than one establishment or worksite) have major implications for the CES redesign with 

respect to sampling unit, stratification, allocation, selection, data collection, and 

estimation. Data tabulated from the BEL indicate that: 

• These employers comprise about 3.6 percent of all private in-scope U.I. accounts, 16 

percent of all worksites (reporting units), and over 35 percent of the total private 

employment. 

• About 93 percent of all private sector multi-site employers operate in only one 2-digit 

SIC and 90 percent are in one 4-digit SIC. Additionally, 99 percent operate in 3 or 

fewer in-scope 2-digit SICs. 

• A total of about 6,000 U.I. accounts with 1,000 or more employees include 215,000 

reporting units (sites) and about 17 percent of employment. There are 23,000 U.I. 

accounts with 250 or more employees and include 425,000 reporting units and about 

25 percent of the total employment. 
 4

• On average, these accounts have sites distributed across 2 to 3 MSAs. 

• There are no clear differences in the pattern of employment change between single 

and multi-site employers. Stratification by single and multi-site accounts does not 

appear to reduce the variance of the estimates. 

V. Sample Design

General parameters-- While both the MSA and industrial estimates are important, the 

consensus among the workgroup members was that the reliability of the total employment 

estimates for States would be the top most consideration. Because of the large sample 

size; the national sample would be the sum of the state samples. Due to budget and 

workload considerations, the sample size for each state would not change from its present 

level; thus reliability would vary from state to state. 

Sampling Unit--One of the first major decision was to determine what the sampling unit 

should be for the redesign; especially for multi-site employers. Possibilities included 

reporting unit, MSA/U.I. account, or U.I. account. We selected the U.I. account to be the 

sampling unit because: (1) it is the unit of reporting by employers at the state level; (2) it 

facilitates tracking of breakouts and mergers; (3) it permits the greatest flexibility in terms 

of subsampling; and (4) it allows a mechanism for capturing opening of new worksites 

for multi-establishment firms. 

Using BEL data, we tabulated over the year changes in employment broken down by 

single worksite and multiple worksite employers for the periods March 1990 to March 91, 

March 91 to March 92, March 93 to March 94, and March 94 to March 95. Even though 

the four periods cover both a recession and an expansion time, the employment from 

opening of new worksites accounted for 0.5 to 0.7 percentage point of the total 

employment. The most important reason for selecting the U.I. account, therefore, is point 

number four mentioned in the above paragraph; failure to capture this growth could lead 

to a gross underestimation that is well beyond the normal acceptable level of 0.2 

percentage point. 

Stratification—For CES, the three primary variables available for stratification are SIC, 

employment size class, and MSA. General research conducted by BLS indicates that 

there is a great deal of variation by size class and by SIC and less so by geography within 

a state. For example, in general, construction industry is more variable than the 

manufacturing industry. Similarly, employers with 1-9 employees have different 

characteristics than those with 250 or more employees. 

Frame Construction—A frame consists of a list of U.I. account numbers within state that 

had employment for any of the months April of the previous year through the latest 

available March. A 2-digit SIC code is assigned to the U.I. account after determining 

which 2-digit SIC has the most employment across all of the reporting units under the 
 5

account; MSA codes are assigned in a similar manner. Each U.I. account is placed into 

one of the following size classes based on its maximum employment for the year. 

1-9; 0-19; 20-49; 50-99; 100-249; 250-499; 500-999; and 1,000 and more employees. 

Allocation cells— Using BEL data, we simulated several alternative sample designs for 

11 states; in each design we chose to minimize the estimated variance for month-tomonth change for total employment at the State level. Some of the designs we tried are: 

2-digit SICs by 8 size classes; MSA by major industry divisions by 8 size classes; 2-digit 

SICs for services industries and major industry divisions for the remaining industries by 8 

size classes; and probability proportion to size with the file sorted by MSA/4-digit 

industry/employment. 

Based on the results of the simulations, characteristics of the multi-site employers, and in 

the interest of limiting number of stratification cells for purposes of allocation (e.g., 

many cells on the frame are empty or have only one U.I. account at a state/2-digit SIC/8 

size class), we chose to stratify by industry division (Mining, Construction, 

Manufacturing--Durable goods, Manufacturing--Nondurable goods, Transportation and 

Public Utilities, Wholesale Trade, Retail Trade, Finance, Insurance, Real Estate, and 

Services) and by 8 size classes as defined above. 

Certainty Stratum—In our original multi-year simulations, we let the optimum allocation 

determine the certainty stratum. In the large states, this was generally size class with 

1,000 or more employees. The participating states wanted size classes with 500 or more 

employees to be absolute certainty because of the importance of large employers for 

MSA estimates. Results of the research indicate that, in general, states gained around six 

percent in efficiency as the certainty class cut-off moved from 500 to 1000, and around 

one additional percent as the cut-off is moved from 1000 to no cut-off. The benefit is, 

however, largely state-specific. Texas, for example gains on average about 19 percent 

from moving the size cut-off from 500 to 1000 and an additional 5 percent from 1000 to 

no cut-off. Illinois, on the other hand, gains only about 2 percent from 500 to an 

unrestricted allocation. The final design has size class with 1000 or more employees as 

the absolute certainty stratum. If the optimal allocation for a stratum requires all of the 

units even if they have under 1,000 employees, they too will be sampled with certainty. 

Cost—At present, we do not have an estimate as to how much it cost to collect a multisite U.I. account vs. a single site account. We have assigned a cost equal to the strata 

average number of UI/County combinations per U.I. number; this cost is utilized in the 

allocation formula. 

Allocation Method—We used an allocation that minimizes the variance for the month-tomonth change for total employment at the state level. For each allocation stratum, Nh is 

the number of U.I. accounts obtained from the current frame, sh is the estimated average 

variance of month-to-month change based on several years of historical data, and the cost 

function is as defined above. Additionally, we imposed the constraint that in each 
 6

allocation cell at least two sample units are allocated provided the cell has two or more 

population units; cells containing one population unit are sampled with certainty. 

Estimated variances are computed as follows. For each allocation stratum and for each of 

the six years from January 1989 to December 1995, we computed a variance of total 

employment for each of the 12 months and 12 month-to-month correlations (one for each 

pair of consecutive months). We then averaged them over the 12 months in a year, 

resulting in six estimates of stratum variances and six estimates of stratum month-tomonth correlation. Within each allocation stratum, we then took the medians of these six 

estimated variances and correlations to compute the estimated average variance of monthto-month change. 

Selection cells—Because of the importance of MSA estimates, we are selecting the 

sample by MSA within each allocation cell at the sampling rate for the allocation cell. 

This provides near proportional allocation across MSAs within any allocation cell. 

Selection weight—While selection of continuing UI accounts is based on near optimum 

allocation, once the sample is selected, the weights are calculated based on the number of 

U.I. accounts actually selected within each allocation cell. It is important to note that 

selection of a U.I. account means all of its reporting units (sites) are selected and assigned 

the same weight. 

Estimation procedures--These procedures including identification and adjustment for 

outliers, definition of modeling industry, and estimators are described more completely in 

a paper by Kirk Wolter and Rachel Harter. Our basic estimator is yw w it it

aty

it

BMF ∑ ∗ ∗ . 

Where, the summation is over the appropriate domain as specified by the user; i is the 

reporting unit (site) level data including the MSA and SIC code; t is the time; y is the data 

type (all employees, production workers, payroll, etc.); waty is the atypical weight 

(selection weight adjusted for outliers); and wBMF is the raked ratio adjustment factor. 

The adjustment factor is: benchmark employment of all non-certainty reporting units 

divided by the sum of the selection weighted benchmark month employment for the noncertainty units in the sample. Two-dimensional raking is performed: state model cells by 

MSA industry division totals. Thus, each reporting unit is assigned an atypical weight and 

a raked ratio adjustment factor. 

Estimates of average weekly hours are derived by taking the ratio of total hours to the 

total of production workers; similarly average hourly earnings is the ratio of total payroll 

to the total hours [West, Grden, Kratzke]. 

Variance Estimation—As of yet, we have not finalized the estimator for the estimates of 

variances. A replication method that accounts for nonrespondents including 

nonparticipants and finite population correction factor is being considered. 
 7

VI. Frame Maintenance 

We are using collocated permanent random numbers (PRN) to: update the probabilities of 

selection; include new U.I. accounts (births); remove old U.I. accounts (deaths); handle 

units that have changed size class, MSA, or SIC; and perform sample rotation. The main 

reasons for using PRN is to achieve the desired sample overlap from one year to the next 

for the CES sample and to minimize the sample overlap between CES and Bureau’s two 

other establishment surveys (Occupational Safety and Health and Occupational 

Employment Statistics). The collocation distributes the PRNs evenly on the 

interval 0 to 1. [See Chapter 9 of Business Survey Methods] 

We begin by assigning to each U.I. account on the frame a PRN; collocating PRNs at 

MSA/3-digit SIC/size class; the frame is then sorted by selection cells, h, and the first nh

units are selected within each stratum. With each quarterly update of the file, new U.I. 

accounts are identified and assigned PRNs; they are then collocated at the CES selection 

stratum level. At this time, we supplement the existing sample with a sample of new U.I. 

accounts (births) and we also remove from the existing sample all the old U.I. accounts 

on the frame that cease to exist (deaths). Our plan is to perform frame maintenance once 

a year as was done in the simulations; at this time, we would resize all the units, reclassify 

them in their proper SIC and MSA, reallocate, and select the entire sample by shifting the 

starting point to achieve the desired overlap. For example, if we wanted to rotate onethird of the units in a stratum, then we would begin the selection after the first one-third 

nh units. As described below, our plan is to rotate one panel at a time. 

Rotation—At present, the plan is to first phase-in the probability sample for all industries. 

Once that is accomplished, the plan is to rotate the sample in size class 1, 2, 3, and 4 (i.e., 

less than 100 employees) provided the sampling weight is greater than 3.000. Our plan is 

to sort the entire file by state/MSA/size class, sequentially assign to each stratum 

numbers 1 to 36 beginning with a random start, and then rotate one panel per month. 

Rotating roughly 1/36 of the sample every month is subject to change depending on the 

cost of enrolling new units. 

VII. Transition Phase

Our plan is to introduce one industry at a time beginning with wholesale trade industries. 

The initial plan is to have a production test that would last anywhere from one to two 

years starting in July 1997; the purpose of this test is to work out operational procedures 

especially with respect to data collection and estimation systems. After that, we are 

planning to phase in the probability sample and rotate out the old sample of one or more 

industry divisions with subsequent years’ benchmark releases. At benchmark time with 

March data, we would revise the employment of all the new and old series to align with 

the ES-202 data. As we rotate new industries, we would add the change from the new 

sample to the levels of the old sample for all estimates except employment. Once the 

probability sample is phased-in, we would take one break in series (e.g., average hourly 
 8

earnings); this way users of the data will get a month-to-month change based on the 

probability sample but not perpetual break in the level of the series. 

VIII. Summary 

 

In summary, the Bureau has taken a major step to move one of its most important surveys 

to a probability basis. While we have attempted to build-in many of the operational 

aspects into our procedures, undoubtedly, we will be faced with several unforeseen 

issues. These issues and others that we have thought of but do not know how to handle 

without more data (e.g., seasonal adjustment) will be dealt with on an ongoing basis.