The Quality vs. Timeliness Tradeoffs in the 

BLS ES-202 Administrative Statistics 

Akbar Sadeghi and Richard Clayton 

Bureau of Labor Statistics 

Abstract 

A primary benefit of using data based on administrative records is comprehensiveness. When timeliness of 

data is an important factor, however, administrative data are usually less advantageous than sample based 

data. By the time that administrative records become completely available (usually after meeting a certain 

deadline), the data are somewhat outdated. Any attempt to expedite the use of such data might result in a 

loss of accuracy. This paper will investigate the possible trade-off between timeliness and data quality in 

the case of the Bureau of Labor Statistics' (BLS) Covered Employment and Wages or ES-202. The 

quarterly ES-202 data are composed of all employers covered by the Unemployment Insurance in the 

United States and represent a virtual census of employment and wages in the country. The data are 

compiled by the state employment security agencies and sent to BLS as part of a Federal/State cooperative 

program. States currently submit their initial micro data files to BLS approximately four months and one 

week following the close of each calendar quarter. The objective of this research is to determine whether 

data quality will decrease as a result of receiving these initial files earlier than the current due dates. Six 

volunteer states submitted their micro data files for each quarter - two weeks prior to the due date, one 

week prior to the due date, and on the due date. The results for the last three quarters of 2000 showed no 

significant quality deterioration as a result of moving up the ES-202 due date. 

Background 

The Bureau of Labor Statistics is the principal statistical agency of the Department of Labor with a mission 

to acquire and disseminate labor market information (LMI). In execution of its mandate, BLS runs 

numerous labor force programs each with different LMI objectives. The Covered Employment and Wages 

Program, commonly known as the ES-202 program, originated with the Social Security Act of 1935 and 

the initiation of the Federal Unemployment Insurance Act in 1938. The ES-202 program authorized BLS to 

collect the information needed by the Department of Labor for overseeing the state unemployment 

insurance programs. Initially, the program covered only private establishments with more than eight 

workers. The subsequent amendment and other Federal legislation extended the coverage to all firms 

employing one worker or more and covered public sector workers in the Federal, State and local 

governments. In 1999, covered employment reported by all sources comprised 97.5 percent of the 

employees on non-farm payrolls and provided a "virtual census" of employment and wages in the United 

States. 

Under the ES-202 program operating procedures, all employers initially file "Status Determination 

Reports" with the states unemployment insurance (UI) tax units by which basic business information and 

classifications are collected. On a quarterly basis, business establishments file "Quarterly Contribution 

Reports" (QCR) with monthly employment and quarterly wages along with UI tax contributions. In the 

"Annual Refiling Survey", the status reports are updated for the changes in industry, geographic and 

ownership codes on a three-year cycle basis with one third of all establishments surveyed each year. The 

states compile initial status reports, Annual Refiling Survey and Quarterly Contribution Reports, and 

deliver them to BLS according to a timetable agreed upon in the annual LMI Cooperative Agreement 

between BLS and the states. Quarterly deliverables from the states are called Enhanced Quarterly 

Unemployment Insurance (EQUI) files and contain the most comprehensive source of information on 

employment and wages across industries and geographical locations1i

. 

ES-202 data have many uses. These data serve as 1) a source of comprehensive macro level employment 

and wage data by industry and geography, 2) the sampling frame for numerous BLS surveys, 3) a

benchmark for critical BLS surveys Current Employment Statistics and Occupational Employment 

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Statistics and 4) an input to the Bureau of Economic Analysis's National Income and Product Accounts. 

ES-202 program also annually publishes average employment and wages aggregated by 4-digit industrial 

classification for the nation, fifty states and the District of Columbia. Data are available to researchers in 

academia and business community on request from BLS and the states. 

ES-202 represents a prime example of the use of administrative records in data collection. It is 

comprehensive, cost effective and independent of public cooperation in contrast to the survey-based data 

gathering. Some of the shortcomings of administrative records, such as the risk of not accurately 

representing the population of interest, the lack of control on data objects and poor quality of coding, are 

not present in ES-202 programii. In fact, the ES-202 data has blended well into other survey-based 

programs at BLS. However, the ES-202 program, like most other data derived from administrative records, 

is not timely. Employers have a month after the close of the quarter to file UI tax contributions. Late filing 

and delinquent records are very common. Therefore, administrative data unlike most sample-based data are 

not readily available. Moreover, the enormity of the number of records makes editing a long and tedious 

process, which could potentially prolong the availability of completed data. In the case of ES-202, BLS 

receives the initial microdata files from the states approximately four months and one week after the close 

of each calendar quarter. 

Objective 

This paper reports the results of an experiment conducted by asking six volunteer states to submit two 

earlier versions of their EQUI files prior to the due date for the second through the fourth quarter of the 

year 2000. We asked the volunteer states to submit their single quarter EQUI micro files two weeks and 

one week prior to the initial due date. It should be noted that there is a final update version that is due a 

month after the initial version. The due date for update EQUI remained unchanged. We selected one state 

from each BLS region and attempted to have states that cover a range of sizes and represent both data 

processing systems. The selected states were Connecticut, West Virginia, Minnesota, Florida, Texas and 

Alaska. Alaska could not participate in the second quarter 2000 (2000/2) but started with the 2000/3 

quarter. 

The objective was to determine whether data quality would be lost as a result of moving up the due date by 

one or two weeks, and to see whether there would be a point when the data quality would deteriorate 

considerably should the due date continue to be moved up. In pursuing that objective, we implicitly 

assumed that there is a diminishing marginal return (in terms of data quality) to the amount of time spent on 

data preparation. Up to a certain point, as more time is spent on editing and data clean-ups total data quality 

Total Data Quality Marginal Data Quality

 Time Time 

should increase but marginally at a lower rate. By the same token, if we wish to enhance the timeliness of 

data by accelerating the due date, less data quality will be lost in the initial unit of time forgone (first twoweek in our experiment.) 

Methodology 

First, we assumed that the counts in the regular due-date version of EQUI are the actual numbers. 

Therefore, the absolute value of the rate of difference between those numbers and the ones in the early 

submittals can be used as a measure of data quality. The lower the difference the higher the data quality. A 

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'no change' is an ideal situation, which means there is no quality loss as a result of moving up the due date. 

The data quality under this definition is synonymous with accuracy. Second, we selected employment 

numbers and wages as the main gauge of data quality to represent the entire universe of data in ES-202 

files. Finally, we focused on two areas that would be impacted directly and immediately by the early 

submission of EQUI files: the level of imputations and number of edit failures. Early submittal may cause 

a higher degree of imputation for delinquent records and a larger number of edit failures due to a shorter 

time available for editing and data clean-up. As the due date is accelerated, there is a potential risk to data 

quality since the level of imputed values may rise for less inclusion of reported data. One of our objectives 

was to measure the change in imputations and their effect on data quality. 

Results 

EQUI files for the last three quarters of 2000 were delivered as planned in three submittals on due dates for 

most states. West Virginia transmitted their files several days prior to the due dates in all three quarters, but 

the interval between submittals was one week like other states. Alaska joined the project in the third 

quarter for the first time so only two quarters of data were available for this state. Florida and Texas did 

not send their second submittal in the fourth quarter on time. We could have obtained the files for these 

two states a few days after the due date, but that would have made the time between the second and third 

submittal less than one week. To make the numbers more consistent across the states, we decided not to 

include the second submittal of the fourth quarter for Texas and Florida. For the same reason, the second 

submittal of the second quarter in Florida was excluded from this analysis. In three instances, all of which 

occurred in the first submittal, mishaps at the data entry stage produced some unusually large numbers. The 

errors were not related to the early submission and seemed so large and obvious that could not go 

undetected under normal delivery. Therefore, we adjusted the numbers for those errors. 

In what follows, we will discuss the results in three main areas of concerns: employment and wage data, 

imputations and edit failures. Employment and wages are the main product of ES-202 program and were 

used as the main gauge of data quality. Imputations were analyzed to determine if an early submission of 

EQUI files would impact the level of imputation and if so, what implications that has for the quality of 

data. Finally, the number of micro edit failures and its impact on data quality are analyzed given the fact 

that an early submittal leaves less time for editing activities. Table 1 shows the changes in the third month 

employment and total wages for all volunteer states in all three quarters over different submittals. 

• Total Employment and Wage Data 

In the second quarter 2000, third month employment, a measure of utmost importance for the ES-202 

program, showed mixed results with respect to changes from the earliest version to the final one. The 

difference between the regular due date employment numbers and the first submittal were low in two 

states (.22 percent in West Virginia and .17 percent in Texas), relatively high in two other states (–2.47 

percent in Florida and 1.6 per cent in Connecticut) and acceptable in one state (.82 percent in Minnesota). 

For a one-week early submittal, the numbers were almost identical in three states and high (-3.59 percent) 

in Minnesota. All states combined, the total third month employment changed only -.5 percent over the 

two weekly submittals. 

In the third quarter, employment numbers changed slightly over the three submittals. Compared to the 

numbers in the third submittal (EQUI initial), the change for a two-week early submittal ranged from a low 

.03 percent in West Virginia to a high .77 percent in Florida. For a one-week early submittal, the change 

ranged from zero in West Virginia to .61 percent in Texas. All states combined, the total third month 

employment changed only .47 per cent over two weeks. 

In the fourth quarter, the numbers in the first and third submittal were extremely close in all states except 

Minnesota where the difference was 5.1 percentiii. In fact, employment in two states showed no change, 

and in two other states changed less than one tenth of one percent. For a one-week early submission, all 

four states for which the data were available, showed close numbers ranging from 0 to .57 per cent. The 

difference between first and last submittal was .56 percent for all states together. 

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In the second quarter, except Florida, all states showed a higher third month employment in the final 

counts compared to the first submittals two weeks earlier, suggesting that an early submittal may 

underestimate the employment figure. In the third quarter, however, four out of the six states showed 

lower employment in the final version compared to the first submittal, suggesting the presence of an 

overestimation as a result of the early submittal. The numbers for the fourth quarter were split with three 

states showing overestimation and three states showing underestimation. The conflicting results could be 

interpreted in two ways. It could represent the stochastic and randomness nature of these numbers with 

no apparent predictable direction. It could also be a result of the seasonal and cyclical factors. The final 

submittals always include an increase in the number of records with "reported" status that substituted the 

Table 1

 Changes of Employment and Total Wages over Three Submittals 

In percents 

Second Quarter Third Quarter Fourth Quarter 1 st to 2 nd

submittal

2 nd to final

submittal

1 st to final

submittal

1 st to 2 nd

submittal

2 nd to final

submittal

1 st to final

submittal

1 st to 2 nd

submittal

2 nd to final

submittal

1 st to final

submittal 

Employment:

 West Virginia .23 -.01 .22 .03 .00 .03 .00 .00 .00 

Minnesota 4.57 -3.59 .82 .59 .04 .63 4.71 .57 5.28 

Texas .15 .02 .17 -.10 -.61 -.71 .00 

Connecticut 1.66 -.06 1.60 -.03 -.02 -.05 -.02 -.03 -.05 

Florida -2.47 -1.18 .42 -.77 -.07 

Alaska .08 -.16 -.08 .00 -.28 -.28 

Total Wages:

 West Virginia .22 .00 .22 .01 .00 .01 .13 .00 .13 

Minnesota .13 .59 .72 .34 -.01 .33 6.30 .43 6.76 

Texas -.02 .08 .05 -.05 .02 -.03 .01 

Connecticut .55 .12 .67 -.36 .04 -.32 -.07 .04 -.02 

Florida .68 .31 .47 .78 .08 

Alaska .16 .0 .16 .0 .0 -.01 

previously "imputed" records. Since imputations are mostly based on the past trends and historical 

values, they may be subject to seasonal and cyclical variations. For instance, in an expansionary period, 

estimates based on past numbers have a tendency for underestimation, while in a contractionary period 

the reverse is true. More observations in the future will shed light on this subject. 

The pattern of change in employment numbers over three submittals in all three quarters of data does not 

appear to be linear. The numbers in the early submittals do not increasingly or decreasingly converge to the 

final due date numbers in a steady and consistent manner. In some instances, total employment increased in 

the second submittal from the first version but decreased in the final count. The bulk of the change in 

employment from the earliest to the final version occurred in the second submittal with little change in the 

last week leading to the regular due date. 

The number of records and employment for the "new" and "dropped" establishments changed significantly 

from one submittal to the next in almost all states and in every quarter. One possible explanation for such 

fluctuation could be the effect of information about successor and predecessor relations that through time, 

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becomes available and causes some records to move into or out of new or dropped status. The fluctuating 

nature of these data can be seen even in the update version of EQUI and will finally settle in the 

Longitudinal Database where "business closing" and "business opening " are more accurately defined and 

compilediv

. 

The wage numbers followed almost the same pattern as the employment data. However, when compared to 

the employment figures, the total wages in the early submittals were closer to the regular due date numbers, 

and there was only one case of an unsatisfactory result. The rate of changes from the early submittals to the 

final due date version ranged from zero to .78 percent, except in Minnesota where in a two-week early 

submittal in the fourth quarter, there was a 6.76 per cent difference. The change from the first to the final 

submittal was mostly distributed between the first and second submittal with less change from the second 

submittal to the final version. The average weekly wage remained flat with no or minimal change 

throughout three submittals. 

• Imputation 

In the case of incomplete data or delinquent filings, states impute for the missing data. Later, when the 

actual data become available, the imputed numbers are replaced by the reported data. To see the impact of 

imputations on data quality, we needed to know if an early submission of EQUI files would cause a higher 

level of imputation, and if so how changes in imputation ratios affected data quality. The results showed 

that the imputation ratios declined across the board for all states over three submittals and in all three 

quarters. The changes, however, were very small which suggests that in all states, at two weeks prior to the 

due date, the flow of incoming late unemployment insurance tax filing reached a considerably slow stage. 

We ran a statistical regression to see whether variations in data quality can be explained by variations in the 

imputation ratios. The correlation coefficient turned out very low, showing no significant association 

between data quality and imputation ratios. The absence of a significant correlation between lower 

imputations and higher data quality means that the imputation methods must have produced accurate results 

with estimated numbers compatible with the actual numbers. In other words, a lower imputation ratio 

simply means that an imputed number has been removed and replaced by the reported data with no impact 

on total employment and wages. 

In order to check the quality of imputation methods, we examined the individual records that were imputed 

in the first submittal and were converted to the reported status in the subsequent submittals. We did that 

comparison for the state of Florida and Connecticut in the third quarter and all states in the fourth quarter. 

With 541 records in the third quarter and 708 records in the fourth quarter, Florida had the largest number 

of imputed records converted to reported status over two weeks. The total reported employment was very 

close to the estimated number. However, there were cases where employment was estimated zero in the 

first submittal but reported with non-zero employment in the final submittal. There were also cases where 

the opposite happened; a large employment number was imputed in the first submittal but reported zero in 

the final submittal. Such disparity should be found mostly in the event of either a change in ownership or a 

change in reporting configuration. A change in reporting configuration happens when a single 

establishment reports as a multi-establishment employer, or a multi-establishment unit consolidates 

operation and reports as a single establishment. In such cases, information about successor or predecessor 

may explain why an establishment with imputed non-zero employment and wages could end up with zero 

employment and wages. We found the presence of successor information in most zero employment 

numbers. 

The pattern of change in imputed records in Florida was found more or less in other states, especially ones 

with a larger imputation ratio and a greater number of imputed records converted to reported status. That 

pattern can be summarized as follows: 1) the total imputed employment and wage numbers were close to 

the actual reported numbers, 2) individual estimated and actual numbers were close in most observations 

while overestimated records offset the underestimated records, 3) there were cases of zero employment in 

both estimated and actual numbers that were converted to non-zero in the subsequent submittal. The new 

information on ownership change, breakout and consolidation of units that states might have received after 

5 
the first submittal could be responsible for the sharp difference between imputed and actual numbers. In 

General, imputations proved to be neutral in terms of data quality at the macro level, especially at low 

ratios and small changes. 

On the same note, Florida in the second quarter had a 22.4 percent imputation ratio, which is high 

compared to the national average and Florida's previous quarters' ratios. A high imputation ratio reportedly 

was caused by a technical problem related to the scanning not delinquency on the part of the employers. 

That problem worsened in the third quarter and apparently was resolved in the fourth quarter. In the final 

update version of EQUI for the second quarter, Florida's imputation ratio dropped to 6.8 percent from 22.4 

percent. Total employment, meantime, rose to 7,112,271 from 6,953,899. The difference, which is 

somewhat high, could be a result of the higher reported data that substituted the imputed records, 

suggesting that a high imputation ratio could have adverse impact on data quality. 

• Edit Failures 

ES-202 data are subject to numerous micro and macro scrutiny initially at the states and finally at BLS. The 

editing process is predominantly a mechanical one. The computer processes data and assigns edit flags 

based on some built-in criteria. The state staff review the edit results and correct the erroneous numbers, 

verify or explain the unusual numbers and check for consistency between Quarterly Contribution Reports 

and Multiple Worksite Reports. The number of micro edit failures, therefore, shows how many records did 

not meet pre-set conditions, parameters and tolerances and are grouped in nine levels based on the purpose 

and severity of the error. 

Virtually in all three quarters under study and in all states, the number of micro edit failures steadily 

declined over the three submittals. The reduction in the total number of edit failures was generally high for 

two weeks between first and last submittal. However, edit failures related to industrial classification (SIC 

and NAICS codes) made a large portion of the total edit flags. Adjusting for SIC codes, the remaining 

number of edit failures showed a decline but at a moderate pace. The number of level five edit messages, 

which counts the establishments with significant changes in employment and wage over the quarter, 

changed little or remained unchanged in three submittals. A constant and sometimes sharp decrease in the 

number of edit failures may indicate that in ES-202 processing flow the last three weeks before the due date 

are the height of editing activities. 

Although the level of edit failures was higher in the early submittals than in the initial due date version in 

all three quarters under study, they were remarkably in line with previous quarter EQUI initial and the 

EQUI of the same quarter a year earlier. 

To see the impact of changes in the number of edit failures and changes in data quality, we performed a 

regression analysis. A statistically significant association was not found between the changes in total edit 

failures (both grand total and non-SIC total) and changes in data quality (measured by the proximity of the 

earlier submittals to the due date version). However, a linear regression of the changes in level five edit 

failures on the changes in data quality had a correlation coefficient (R Squared) of .60 and statistically 

significant coefficients. 

Conclusion 

We reviewed and analyzed three snapshots of ES-202 data on a one-week-apart basis in three quarters and 

six states. The results generally showed a lack of significant quality drop as a result of moving up the due 

dates by one or two weeks. We found a few cases of larger-than-expected deviation from the initial duedate numbers, such as Florida and Connecticut in the first and Minnesota in the fourth quarter. Those cases 

were not time-related and could not go undetected under any processing system. The absence of a 

significant quality loss should be attributed to the fact that the experiment with expediting the ES-202 

process was done at a late stage in ES-202 timeline, when the marginal improvement on data quality with 

respect to time was minimal. Generally speaking, a longer time spent on data preparation brings a higher 

data quality, but marginal return in terms of data quality diminishes as more time are used in the process. 

6 
 

For that reason, speeding up the process by one or two weeks did not cause appreciable quality 

deterioration. In fact, an early submittal by one week produced numbers generally closer to the actual due 

date numbers than the same data in a two-week early submission. 

Imputation for delinquent records, which showed slight changes over the three submittals, seems to have no 

apparent implications for data quality, especially at macro level and low ratios. However, new information 

about the predecessor and successor relation or changes in the status of the establishments as 'continuous', 

'new' or 'dropped ' in case of imputed records can create a sharp gap between imputed and actual values. 

Such information becomes available on a continuous basis over the course of time. The total number of edit 

failures declined in each submittal but seemed to be neutral in terms of changes to economic data. The level 

five edit failures, which deals directly with employment and wage numbers, showed rather significant 

correlation with data quality. 

In sum, the observations in this project to date support the idea of moving up the due date by one or two 

weeks. An extension of this experiment with a larger number of states over fiscal year 2002 will bring more 

evidence on the plausibility of expediting the processing flow of ES-202 data. 

i

 "ES-202 Operating Manual" U.S. Department of Labor, Bureau of Labor Statistics, July 2000.

ii For more on the uses of administrative records see Ron Provost and Charlene Leggieri "Expansion of 

Administrative Records uses at the Census Bureau: A Long-Range Research Plan" Paper presented at the 

Meeting of the Federal Committee on Statistical Methodology, Washington D.C. November 1999.

iii Minnesota's numbers in the first submittal of the fourth quarter were underreported due to some records 

that were erroneously coded as missing. Those records which were subunits of multi establishment 

employers should have prorated or imputed from the master records. The error was discovered and 

corrected in the succeeding submittal. Adjusting for this seemingly detectable error, the results for the 

fourth quarter were very close in all submittals. 

iv See Timothy R. Pivetz, Michael A. Searson, James R. Spletzer. "Measuring Job Flows and the Life Cycle 

of Establishments with BLS Longitudinal Establishments Microdata", Monthly Labor Review, Vol. 124, 

No. 4, April 2001. 

7