INVESTIGATION OF SELECTIVE EDITING 

PROCEDURES USING QUINQUENNIAL DATA1 

Katherine Jenny Thompson and Susan Lynn Hostetter 

U.S. Bureau of the Census, Department of Commerce 

Abstract 

Selective editing scores individual questionnaires based on their potential effects on the estimates, selecting only the 

cases with a high probability of impact on tabulations for analyst referral. In 2000, we conducted an investigation into 

the feasibility of using selective editing methods on an annual survey with the ultimate goal of developing a selective 

editing methodology for use in the 2002 Economic Census. This paper applies our recommended approach to 

quinquennial Economic Census data, used at both the macro and micro levels and concludes with recommendations for 

the 2002 Economic Census programs. 

Keywords: Edit referral, score function 

1. Introduction

Every five years, the U.S. Bureau of the Census conducts a census of businesses. This Economic

Census provides an important framework and benchmark for composite measures such as the gross

domestic product estimates, input/output measures, and production and price indexes. Economic

Census tabulations profile the U.S. economy from the national to the local level at a more detailed

industry and geographic area level than the more frequently collected statistical series that measure

short-term changes in the economy. Moreover, Economic Census micro-data is used to construct

frames for sample surveys and is used as input for economic modeling.

Economic Census data is reviewed in many different ways before publication. The first review is the 

micro-level review of edit-failing records. Typically the records are reviewed extensively at this 

point with thousands of records being referred to each analyst to review, one by one. After microreview is completed, analysts begin table cell analysis (macro-review). In many programs, selected 

individual records undergo a third review, reconciling reported census data to data collected from 

the same units in current annual surveys. Finally, there is another stage of micro-review of data used 

for frame construction. The multiple phases of this review process can be quite fatiguing for the 

analysts, which in turn can affect data quality. 

This study came about as an effort to improve the efficiency of the first micro- review of edit-failing 

records. The Economic Census is administered by nine different program areas. Currently, 

procedures for determining which edit-failing records should be micro-reviewed differ by program 

area. Our goal is to determine whether selective editing can replace these individual census 

programs’ edit referral procedures. Selective editing determines edit referrals based on the 

questionnaire’s potential effects on the estimates, isolating the edit failures with the largest expected 

impact by using score functions with predetermined critical values. Critical values are computed 

from a prior survey/census cycle so that processing is not delayed by their calculation. During the 

actual editing cycle, records with scores that exceed their associated critical value are referred to 

analysts; all others are automatically imputed. Selective editing is applied only to records with nonfatal edit errors (response items altered by the edit). Fatal edits – such as blank required items or 

1

The authors thank Roger Goodwin, Carol King, Richard Sigman, and Brandy Yarbrough for their helpful 

comments on earlier versions of this manuscript. This paper reports the results of research and analysis undertaken 

by Census Bureau staff. It has undergone a Census Bureau review more limited in scope than that given to official 

Census Bureau publications. This report is released to inform interested parties of ongoing research and to 

encourage discussion of work in progress. 
. . . 

IYc -Y1ool/Y100 Ye 

failed industry classification – must always be resolved clerically. The term “selective editing” is 

a slight misnomer, or at least misleading, since all reports are machine edited. A better choice of 

words might be “selective referral” or “selective analyst review.” 

Thompson and Hostetter (2000) presents a feasibility study on using selective editing methods on 

an annual survey conducted by the U.S. Bureau of the Census. Quinquennial data vary more than 

annual data due to inflation effects, company failures, and acquisitions. Moreover, different 

programs have different screening criteria for analyst referrals (e.g., some programs include fullimpute records in micro-review, while others do not), so the type of data used to develop critical 

values can greatly differ by program. Any selective editing procedure used for the Economic Census 

must therefore be fairly insensitive to changes over a five year period and must be robust to different 

referral eligibility rules. Additionally, it should not reduce the quality of the edited micro-data. This 

paper addresses these issues, investigating the selective editing methodology proposed in our earlier 

study on quinquennial data from the 1992 and 1997 Census of Construction and from the Census 

of Services-Sectors Businesses. We present the results of our investigation and conclude with 

recommendations for the 2002 Economic Census programs. 

2. Selective Editing Methodology 

Selective editing calculates a single measurement (a global score) for each respondent after fatal edit 

errors have been resolved. Global scores are compared to cell-specific critical values, and the cases 

whose global score exceeds this critical value are targeted for analyst referral. Examples of selective 

editing cells include industries, or size-class-categories within industry. Cases that pass all edits 

have a global score of zero. Critical values are computed from prior-period data using the same 

global score function. 

Global scores combine local scores, which are calculated for individual questionnaire items. Local 

scores measure the magnitude of change between the reported and edited value of selected data 

items. Because all local scores are combined into one global score per reporting unit, local scores 

must be based on the same units of measurement. For example, number of employees is made 

comparable to other items such as annual payroll or total receipts by multiplying the employment 

data by an industry’s average earnings ratio (wages/employment) before calculating its local score. 

We used the global and local score functions recommended by Thompson and Hostetter (2000). Our 

local score function for each data item i is given by 

where ri is the reported value of item i, ei is the edited value of item i, Vi is the industry average for 

(3.1) 

data item i if that item is not reported in dollars (Vi = 1 otherwise), and zi is an 0/1 indicator value 

for reported items that are considered edit failures (definitions of edit failures vary by program). Our 

global score is the maximum value of the establishment’s local scores multiplied by the sample 

weight (/ 1 for census data). So, a large edit change to any variable can potentially cause a referral. 

We used the “simulation study” approach to develop critical values (Lawrence and McKenzie, 

2000). Using 1992 data, we calculate the following two statistics at five percentiles p (p = 45, 55, 

65, 75, 85) of the global score distribution in the selective editing cell: 

! Absolute pseudo-bias, calculated for each data item as , where is the estimate 
of the item total calculated by replacing all reported values with a global score function 

larger than the critical value by their edited values and leaving reported values in place for 

the others, and is the corresponding total calculated from 100% edited data (Latouche 

and Berthelot (1992)); and 

! Referral rates, calculated as the number of establishments with a global score greater than 

the critical value divided by the total number of establishments in the industry. 

We try to balance obtaining low pseudo-bias for each item and maintaining low referral rates (each 

program has its own acceptable referral rate level). When our critical values are successful, the 

pseudo-bias of each item at the critical value is “high” (say 0.05), the pseudo-bias at all preceding 

global score values is near zero, and the referral rate does not exceed the program threshold. 

Because analyst resources are limited, we relax our pseudo-bias criterion if necessary rather than 

increase our referral rate cut-off limit. We evaluate our selected 1992 data critical values by 

calculating corresponding pseudo-biases and referral rates on the 1997 census data. 

In a production environment, the critical values must be computed before the current data is 

received. For the 2002 census, we would use the same methods on 1997 data to develop critical 

values. Our earlier paper recommended using a two-level flagging system, where records with a 

global score greater than the prior-period percentile p must be reviewed by an analyst, and the 

records whose global score is greater than the (p-10)

th percentile (but less than the pth percentile) are 

reviewed as time permits. This approach is quite reasonable and is what we recommend using for 

the 1997 census, selecting p based on the 1992/1997 data patterns. See Section 4 for more details. 

3. Case Study 

This study uses 1992 and 1997 data from the Census of Construction Industries (CCI) and from the 

Census of Services-Sectors Businesses (CSSB). These two programs represent both ends of the 

Economic Census data product spectrum. The CCI data is collected to produce (macro-level) 

tabulations. CSSB data is used at both the macro and micro levels. 

The CCI is a mail-out/mail back sample survey of approximately 130,000 employer businesses from 

the construction sector, publishing industry-level tabulations at the national and state levels. Analyst 

referrals were limited to records with large edit changes to non-zero reported values for at least one 

item [Note: automatically-corrected data items reported in the wrong units – “rounded items” – 

are also excluded from analyst referral]. Imputations for unreported values were not referred. We 

use the same referral screening criterion for our global score assignments (only eligible cases get 

non-zero global scores). We examined fifteen industries selected by subject-matter experts and use 

four highly correlated data items to develop global scores: total receipts; annual payroll; cost of 

construction work subcontracted out; and cost of materials, components, and supplies. 

The CSSB is a mail-out/mail-back census of over four million businesses. This census comprises 

six trade areas: Retail Trade; Wholesale Trade; Service Industries; Transportation, Communication, 

and Utility Industries (Utilities); Finance, Insurance, and Real Estate (FIRE); and Auxiliary 

Establishments. Each trade area publishes industry-level statistics at the national, state, and county 

levels. Any large change from a reported value (excluding rounded items) could trigger an analyst 

referral, although generally analyst referrals were restricted to large establishments. In contrast to 

the CCI program, full-impute cases and unreported values were also candidates for analyst referrals. 

We examined 26 industries (six in Retail Trade; five in Wholesale Trade; seven in Services 
Industries; five in FIRE; and three in Utilities) and used four data items to develop global scores: 

total sales/receipts, annual payroll, first quarter payroll, and number of employees. Originally, we 

included full-impute cases in our global score calculations. However, this greatly decreased the 

selective editing’s effectiveness. By definition, all edit changes to full-impute cases are “large.” 

The distributions of full-impute records are very different from the reporting/keying error 

distributions of reporter units. Mixing the two types of records was not worthwhile. 

4. Industry Level Tabulation Results 

The appendix presents average pseudo-biases and referral rates (and associated SEs) for 1992 and 

1997 CCI and CSSB data (broken down by trade area) using the 1992 global score distribution 

percentiles. Pseudo-biases and referral rates exclude full-impute cases and “rounded” data items, 

yielding conservative estimates. For Construction, using the 65th percentiles of the 1992 global score 

distributions on 1992 data yielded average three-percent referral rates and pseudo-biases of less than 

one percent (all items); corresponding statistics are even lower when we applied the 1992 65th 

percentile to the 1997 data. For all of the CSSB programs except Wholesale, using the 85th 

percentiles of the 1992 global score distributions on 1992 data yielded average referral rates of four 

percent or less and kept most of the average pseudo-biases to less than five-percent. These patterns 

are repeated when the 1992 critical values are applied to the 1997 data with two exceptions: in 

FIRE, the average pseudo-bias for 1st quarter payroll is greater than 5-percent for all 1992 percentiles 

greater than 65; and in the Retail trade department store industry, where all 1992 percentiles yielded 

larger than 30-percent referral rates. For Wholesale, using the 75th percentile of the 1992 global 

score distribution seems to best balance low average pseudo-biases and referral rates on 1992 data; 

applying the 1992 critical value to the 1997 data improves the referral rates. 

These results were suspiciously promising. We expect industry distributions of data items to change 

between censuses. Was it possible that distributions of reporting/keying errors could remain similar 

from census to census, while the actual data item distributions did not? Table 1 contains twosample Wilcoxon test results comparing differences between 1992 and 1997 industry-level global 

score distributions ("=0.05) using location shift alternatives. 

Table 1: Test Results and Critical Value Comparison for Industry-Level Global Score Distributions 

Program Total 

Industries 

Location Shift

 Appropriate 

Different Global Score Distributions 

(" = 0.05) 

1992 critical value percentile (p) 

less than

 1997 (p+10)

th percentile 

CONSTRUCTION 

RETAIL 

WHOLESALE 

SERVICES 

FIRE 

UTILITIES 

15 

6 

5 

7 

5 

3 

14 

6 

5 

7 

5 

2 

8 (3 with 2 > 0, 5 with 2 < 0) 

3 (3 with 2 > 0) 

4 (4 with 2 < 0) 

6 (4 with 2 > 0, 2 with 2 < 0) 

5 (5 with 2 < 0) 

1 (1 with 2 > 0) 

13 

6 

5 

7 

5 

3 

In most industries, the global score distributions are significantly different. Prior to testing, we 

verified that such alternatives were appropriate by viewing overlaid global score distribution 

function graphs and by comparing corresponding percentile differences (i.e., pi,97 - pi,92, where pi

 is 

a percentile. Consistently positive differences imply that the location shift 2 is greater than 0 and 

consistently negative differences imply that 2 < 0). In most cases, the two distribution function 

graphs were indistinguishable. And, in general the percentile differences had the same sign (within 

industry) up to both distributions’ 95th percentiles. When we excluded all cases above the (1992 or 
1.2 ~ ----------------------~ 

0.8 

0.6 

0.4 

0.2 

,... 

o-----~--------------------1 

0.9 

OB 

0.7 

0.6 

0.5 

0.4 

0.3 

02 

0 .1 

0 20000 40000 60000 80000 100000 120000 

G obal Score Values 

1--------- 1992 Global Score ---- 1997 Global Score I 

0 -.-----.------,------------,---------i 

0 100 200 3)0 '!JO 500 

Global Sco re Values 

I___._ 1992 Global Score ---- 1997 Global Score I 

1997) 95th percentile in both data sets, the Wilcoxon tests were still significant, confirming the 

existence of location shifts. Table 1 presents the results of these comparisons, along with location 

shift signs as appropriate. Notice that 16 of the 27 significantly different global score distributions 

have negative location shifts. However, the consistency between 1992 and 1997 referral rates and 

pseudo-biases within industry implies that these location shifts are generally negligible. 

Previously, we concluded that global score distributions from the current and prior data collection 

periods had to be statistically equivalent for selective editing to be effective. Obviously, these 

results contradict this. Our original conclusion was too limited. Selective editing works well when 

the distributions of global scores have the same shape and a “small” location shift up to a cut-off 

value between consecutive time periods. The critical value for the global score should be less than 

this cut-off value. It is not necessary – or often even possible – for the two global score 

distributions to be equivalent. Selective editing predicts where outliers are located in the global 

score distribution. In each census, a high percentage of the automatically-corrected errors are keying 

or balancing errors (e.g., corrected transposed digits, replaced reported total with associated sum of 

details). These types of data corrections often have little effect on the tabulations. Consequently, 

it is not unreasonable to assume similar global score distributions for current period and prior period 

data up to a certain percentile (say, the 90th percentile). 

Figure 1: CDF Plots of 1992 and 1997 Global Score 

Distributions in a Construction Census Industry 

Figure 2: CDF Plots of Truncated 1992 and 1997 

Global Score Distributions in the Same Industry 

Figures 1 and 2 illustrate this, presenting the 1992 

and 1997 global score distributions for a CCI 

industry. The two plotted distributions are 

statistically different. Figure 1 plots the entire 

cumulative density function of both score 

functions. It is impossible to see any differences 

in distribution because the two curves are so 

close; however, the 1992 scores are consistently 

lower than the 1997 scores until the 95th 

percentile of each distribution. Truncating each 

curve at a global score value of 4002 (Figure 2) 

shows the positive location shift between the two 

curves. Because the shift is positive, no accuracy 

is lost using the 1992 critical value with the 1997 

data. In both distributions, the true outlier 

observations lie beyond their respective 95th 

percentiles. Our critical values are set well before 

the 1992-data 95th percentile value to prevent 

overly-high pseudo-biases, so they identify all 

1997 data outliers. 

In our test industries, a high percentage of the 

significantly different global distribution 

functions appear to have negative location shifts 

(i.e., 1997 global score values tend to be smaller 

than the 1992 global score values). These 

location shifts are small; the two sets of curves are 

2

the 78th percentile of the 1997 distribution and the 85th percentile of the 1992 distribution 
Ji 

very close. In all but two of our industries, the 1992 critical value percentile (p) is consistently 

smaller than the (p + 10)

th percentile of the corresponding 1997 distribution. Using a slightly lower 

value of p than warranted by the 1992 pseudo-biases/referral rates helps ensure that all of the outliers 

greater than the 1997 (p + 10)

th percentile are flagged for analyst review, regardless of location shift 

sign. For example, to ensure that the all 2002 global score values greater than the (2002) 85th 

percentile are flagged, we would flag all records with a global score greater than the 1997 75th 

percentile for definite analyst review, with all records with a global score between the 1997 65th and 

75th percentiles to be reviewed as time permits. If referral rates are too high (or to low), we would 

adjust our (1997) “must” percentile accordingly. 

5. Investigating the Effect of Selective Editing on Micro-data 

Selective editing is really designed for programs that collect data solely for tabulation purposes. The 

integrity of the micro-data is not a requirement. The CSSB Economic Census data are used, 

however, at both macro and micro levels, so it is important to examine the effect of selective editing 

at the micro-level as well as the macro-level. Industry-level selective editing cells are probably 

sufficient for accurate tabulation, but may be inadequate for frame development: for example, small 

establishments with “large” edit changes can greatly impact sample survey stratification and 

allocation. We hoped that by refining the selective editing cells, we could develop critical values 

that preserved both the macro and micro-data. 

This section describes our investigation of this approach in the Retail, Wholesale, and Services trade 

areas. Census data from these three trade areas are used to construct frames for annual and monthly 

surveys. These surveys use company data or employer identification number (EIN) data as sample 

units, not individual establishments (Kinyon et al, 2000). These sample units are aggregated 

establishment data: a company is comprised of all establishments under common ownership; and an 

EIN sample unit is comprised of all establishments within a company that use the same EIN to file 

payroll withholdings. Census micro-review is establishment-based. Questionnaires must be edited 

and reviewed as received. Analysts cannot wait to receive all questionnaires from a company or an 

EIN to begin review. Consequently, we could not use the surveys’ strata boundaries to define our 

smaller selective editing cells. An alternative option would have been to use the census programs’ 

imputation cells – Services and Wholesale use legal form of organization, tax status, and type of 

operation to futher classify establishments within industry for imputation – but the classification 

variables used to define these cells were not available to us (and for Retail, the industry is the 

imputation cell). 

Instead, we developed size-class cells (based on establishment data) within industry. Our goal was 

to examine the feasibility of this approach, not to recommend a method of developing size-class 

cells. In a production system, the selective editing and imputation cells should be the same to reduce 

parameter overhead (e.g., development, maintenance, and application). For this portion of the study, 

we developed a (maximum of) seven size-class-within-industry selective editing cells from the 1992 

edited data. One size-class cell contained all establishments that exceeded the 90th percentile of the 

industry’s total annual payroll distribution, thus assuring that all “large” establishments had a chance 

of being reviewed. We used the Sweet and Sigman GUS program (1994) to implement the DaleniusHodges cum rule (Cochran, 1977) with annual payroll as the stratification variable to create the 

other six cells. [Note: using sales to define size-classes yielded much higher and more variable 

industry-level pseudo-biases]. 
Ji 

Then, we repeated the process described in section 4 to develop critical values for each cell. For 

simplicity, we used the same percentile (the program-specific percentile recommended in section 4) 

in all size-classes within an industry as a critical value. We verified our results using industry-level 

pseudo-biases and referral rates, making sure not to improve the data quality at the cost of greatly 

increased referral rates. Recall that we are focusing on the first stage of micro-review, which works 

towards production of quality industry-level tabulations given tight time-constraints. At this stage 

of the process, analysts confine their review to cases for which large edit changes should impact 

tabulations (small establishments may not receive the same scrutiny). Often, during stratification, 

these large establishments become self-representing. Stratification and allocation are consequently 

more affected by the smaller establishments. “Strata jumpers” – establishments classified into the 

wrong strata – can greatly affect the variances estimated from the survey data, making them much 

larger than the target variances used to design the sample. Similarly, “missed” large data errors can 

affect allocation by (falsely) increasing certain within-stratum variances. 

To assess the effect of selective editing on frame construction, we constructed three separate 

establishment-level frames from our 1997 census Retail, Wholesale, and Services data. Frame 1 

consisted entirely of final edited values (no selective editing), representing “truth.” Frame 2 

consisted of industry-cell selectively edited values. Edited values are used for full-impute cases, 

rounded cases, and cases whose global score exceeds the industry-level critical value; reported data 

is used otherwise (similar to the data sets used to compute absolute pseudo-biases). Finally, frame 

3 consisted of industry × size-class cell selectively edited data, constructed similarly to the industrycell level frame. 

We compared the Neyman allocations obtained from each frame for a stratified simple random 

sample without replacement design. We used six strata3

 per industry, stratifying on sales/receipts 

(the stratification variable used for the current surveys), applying the cum to define strata on the 

1997 data . Given each c.v. constraint (0.01, 0.05, 0.10), we compared the sample-size produced by 

three different allocation variables -- sales, annual payroll, and employment – selecting the largest 

of the three allocations. This allowed us to assess the impact of selective editing on the micro-level 

for three of our four variables [Note: 1

st quarter payroll is so highly correlated with annual payroll 

that we did not evaluate it separately]. 

Using selectively edited data – either industry-cell or industry by size-class cell – did not affect the 

strata boundaries in this test: all three sets of strata boundaries were very close. Choice of selective 

editing cell did, however, greatly affect allocation. Table 2 presents the average industry-level 

percent change in sample size from fully-edited establishment data for both the industry-cell 

selectively edited establishment data (frame 2) and the industry by size-class cell selectively edited 

establishment data (frame 3), along with the range of absolute percent difference [Note: these percent 

differences were all positive, except for one Services industry, whose percent change was 

approximately -0.08(%) for all c.v.’s]. Due to the small number of test industries, one large percent 

change can greatly affect a trade area’s mean, so Table 2 also contains counts of industries that have 

less than five percent change from fully-edited data. 

Notice the effect of selective editing cell on allocation. Using industry-level selective editing cells 

generally leads to unacceptably large increases in required sample sizes. The increased allocation 

is due to (relatively) large differences between selectively-edited data and fully-edited data in a small 

3As recommended by Cochran (1997) for variables whose correlation is generally less than 0.95. 
establishment stratum. Although these differences are proportionally small overall (hence the low 

pseudo-biases), they greatly increase the within-strata variability. In contrast, the allocations from 

industry by size-class selectively edited (frame 3) data are generally very close to those obtained 

from the fully-edited data. This includes Retail, whose Table 2 results are a bit misleading. The 

high average percent increase in all categories is caused by very poor results in one industry. If 

omitted, then the average percent increase for Retail ranges from 40 to 49 percent in the industrylevel cells and from 0.4 to 0.5 percent in the industry by size-class cell. 

Table 2: Percent Absolute Difference in Allocated Sample Sizes (Relative to Fully Edited Data) 

RETAIL (6 Industries)

 Mean (minimum, maximum)

 No. of Industries with < 5% change 

WHOLESALE (5 Industries)

 Mean (minimum, maximum)

 No. of Industries with < 5% change 

SERVICES (7 Industries)

 Mean (minimum, maximum)

 No. of Industries with < 5% change 

c.v. = 0.01 

Industry Industry 

By Size 

68 (3, 212) 34 (0, 202) 

1 5 

21 (3, 56) 3 (0, 9) 

2 3 

100 (0, 486) 1 (0, 4) 

3 7 

c.v. = 0.05 

Industry Industry 

By Size 

360 (5,1919) 311 (0, 1865) 

1 5 

63 (4, 250) 5 (0, 13) 

1 3 

159 (0, 851) 2 (0, 4) 

3 7 

c.v. = 0.10 

Industry Industry 

By Size 

450 (5, 2454) 397 (0,2381) 

1 5 

72 (4, 292) 6 (0, 14) 

1 3 

170 (0, 926) 2 (0, 4) 

2 7 

Why were the allocation results so poor in this particular Retail industry? In one small-establishment 

stratum, there were several “large” differences between selectively-edited data and fully-edited data 

for annual payroll. These differences did not impact the industry-level tabulations; in fact, the 

industry-level pseudo-bias for the frame 3 annual payroll data is low, around three percent. So, the 

selective editing process here resulted in within-stratum variance approximately ten times larger than 

the “true” within-stratum variance (constructed from fully edited data). This problem is undetectable 

at the macro level. Moreover, the within-strata variability for sales and employment using the frame 

3 data in the same stratum are quite reasonable. This indicates that further review of cases in smallestablishment strata is probably necessary when using selective-edited microdata. 

The current surveys use aggregate data, not establishment data. Small differences at the individual 

establishment level are potential large differences when aggregated (for example, one grocery store 

establishment in a chain may not comprise a large percentage of an industry tabulation, but the 

aggregated company data might). Table 3 examines the effect on allocation of using industry by 

size-class selectively edited establishment data to create a frame of EIN-unit data, again compared 

to data from an frame of EIN-units constructed from fully edited data. Once again, the selective 

editing had little – if any 

Table 3: Percent Absolute Difference in Allocated Sample Sizes with – effect on stratification. 

EIN Data (Relative to Frame Constructed from Fully Edited Data) 

The aggregated data 

results reinforce our 

earlier findings. By 

carefully defining our 

selective editing cells 

within industry, we 

reduce the number of 

l a r g e difference s 

between selectivelyc.v. = 0.01 c.v. = 0.05 c.v. = 0.10 

RETAIL

 Mean (minimum, maximum)

 No. of Industries with < 5% change 

63 (0, 376) 

5 

333 (0, 1997) 

5 

293 (0, 1755) 

5 

WHOLESALE

 Mean (minimum, maximum)

 No. of Industries with < 5% change 

2 (0, 6) 

3 

3 (0, 9) 

3 

3 (0,10) 

4 

SERVICES

 Mean (minimum, maximum)

 No. of Industries with < 5% change 

1 (0, 2) 

7 

1 (0, 2) 

7 

1 (0, 2) 

7 
 

edited and fully-edited data within size-category at the establishment level. Note that the same 

pattern for the problem Retail trade industry appears here. 

6. Conclusion 

This study investigated the use of our previously recommended selective editing methodology on 

quinquennial census data. We examined the effect of selective editing at both the macro and micro 

levels on data from two different programs, each of which has its own screening criteria for analyst 

referral cases. The results from this study confirm our earlier findings: namely, that selective editing 

using our recommended score function has desirable properties in most cases (low referral rates and 

low pseudo-biases) because of the consistent reporting/keying error patterns between censuses. The 

effect of inflation/deflation – a major concern – turned out to be negligible, at least in our sample 

industries. Moreover, our score functions and critical value selection methodology are fairly robust 

to different screening criteria as long as at least one data item is reported. 

We strongly believe that this methodology should be pursued for the 2002 Economic Census 

although there are a few further areas that must be investigated for production implementation. First, 

our method does not work with selective-editing cells containing less than 20 observations (both in 

current and prior periods). This was not an issue with this study’s test industries but could be in the 

future. Second, we need to develop size-class-within-industry selective editing cells that are 

consistent with imputation cells for programs that use the micro-data as well as the macro-data. 

After determining the selective editing cells, we can use the same approach described in section 3 

to develop critical value percentiles (although implementors may not want to institute a two-tiered 

flagging system in the small establishment cells). Finally, we need to develop a “fall-back” plan 

for industries whose critical values result in overly-high or overly-low referral rates (those industries 

whose reporting/keying error distributions change greatly between collection period). Such finetuning will result in a product with wide program-application potential that will save analyst 

resources while preserving data quality. 

References 

Cochran, W. (1977). Sampling Techniques. New York: Wiley. 

Kinyon, D.; Glassbrenner, D.; Black, J.; and Detlefsen, R. (2000). Designing Business Samples for 

Surveys Conducted by the United States Bureau of the Census. Proceeding of the Second 

Annual Conference on Establishment Surveys. 

Latouche, M. and Berthelot, J.M. (1992). Use of a Score Function to Prioritize and Limit Recontacts 

in Editing Business Surveys. Journal of Official Statistics, 8, pp. 389-400. 

Lawrence, D. and McKenzie, R. (2000). The General Application of Significance Editing. Journal 

of Official Statistics, 16, pp. 243- 255. 

Sweet, E. and Sigman, R. (1995). User Guide for the Generalized SAS Univariate Stratification 

Program. U.S. Bureau of the Census: ESM-9504. 

Thompson, K.J. and Hostetter, S (2000). Investigation of Selective Editing Procedures for The U.S. 

Bureau of the Census Economic Programs. Proceedings of the Second International 

Conference on Establishment Surveys. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix: Average Pseudo-Biases and Referral Rates 

Data Items 45th Pe rcen tile 

(Pseudo bias/ 

standard error) 

55th Pe rcen tile 

(Pseudo bias/ 

standard error) 

65th Pe rcen tile 

(Pseudo bias/ 

standard error) 

75th Pe rcen tile 

(Pseudo bias/ 

standard error) 

85th Pe rcen tile 

(Pseudo bias/ 

standard error) 

CONSTRUCTION 

Total Receipts 1992

 1997 

Annual Payroll 1992

 1997 

Cost of Const. Work Out 1992

 1997 

Cost of Mat., Comp. and Sup. 1992

 1997 

Referral Rate 1992

 1997 

RETAIL 

Total Sales 1992

 1997 

Annual Payroll 1992

 1997 

1

st Quarter payroll 1992

 1997 

Total Employment 1992

 1997 

Referral Rate 1992

 1997 

WHOLESALE 

Total Sales 1992

 1997 

Annual Payroll 1992

 1997 

1

st Quarter payroll 1992

 1997 

Total Employment 1992

 1997 

Referral Rate 1992

 1997 

SERVICES 

Total Sales 1992

 1997 

Annual Payroll 1992

 1997 

1

st Quarter payroll 1992

 1997 

Total Employment 1992

 1997 

Referral Rate 1992

 1997 

FINANCE, INSURANCE,REAL ESTATE 

Total Sales 1992

 1997 

Annual Payroll 1992

 1997 

1

st Quarter payroll 1992

 1997 

Total Employment 1992

 1997 

Referral Rate 1992

 1997 

UTILITIES 

Total Sales 1992

 1997 

Annual Payroll 1992

 1997 

1

st Quarter payroll 1992

 1997 

Total Employment 1992

 1997 

Referral Rate 1992

 1997 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.00 / 0.00 

0.04 / 0.01 

0.06 / 0.03 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.05 / 0.01 

0.14 / 0.01 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.01 / 0.00 

0.03 / 0.02 

0.00 / 0.00 

0.00 / 0.00 

0.10 / 0.04 

0.07 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.00 / 0.00 

0.00 / 0.00 

0.04 / 0.03 

0.07 / 0.04 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.01 

0.04 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.07 / 0.01 

0.11 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.01 

0.00 / 0.00 

0.00 / 0.00 

0.14 / 0.10 

0.15 / 0.06 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.02 / 0.01 

0.01 / 0.00 

0.01 / 0.00 

0.00 / 0.00 

0.03 / 0.01 

0.05 / 0.03 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.04 / 0.01 

0.12 / 0.01 

0.01 / 0.00 

0.00 / 0.00 

0.01 / 0.01 

0.01 / 0.00 

0.02 / 0.01 

0.04 / 0.02 

0.00 / 0.00 

0.01 / 0.00 

0.08 / 0.03 

0.06 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.04 / 0.02 

0.06 / 0.03 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.01 

0.04 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.06 / 0.00 

0.09 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.01 

0.00 / 0.00 

0.00 / 0.00 

0.11 / 0.08 

0.12 / 0.05 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.04 / 0.02 

0.01 / 0.02 

0.02 / 0.00 

0.01 / 0.00 

0.03 / 0.01 

0.04 / 0.02 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.00 / 0.00 

0.02 / 0.00 

0.03 / 0.00 

0.11 / 0.01 

0.01 / 0.00 

0.00 / 0.00 

0.01 / 0.01 

0.01 / 0.00 

0.02 / 0.01 

0.04 / 0.02 

0.00 / 0.00 

0.01 / 0.00 

0.06 / 0.02 

0.05 / 0.01 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.03 / 0.02 

0.05 / 0.03 

0.01 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.01 / 0.00 

0.05 / 0.01 

0.01 / 0.00 

0.02 / 0.01 

0.04 / 0.00 

0.06 / 0.01 

0.00 / 0.00 

0.02 / 0.01 

0.01 / 0.01 

0.01 / 0.01 

0.00 / 0.00 

0.02 / 0.02 

0.00 / 0.00 

0.01 / 0.01 

0.09 / 0.07 

0.11 / 0.05 

0.00 / 0.00 

0.00 / 0.00 

0.03 / 0.02 

0.01 / 0.00 

0.15 / 0.15 

0.03 / 0.02 

0.05 / 0.00 

0.02 / 0.01 

0.02 / 0.00 

0.03 / 0.02 

0.01 / 0.01 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.00 / 0.00 

0.02 / 0.01 

0.02 / 0.00 

0.10 / 0.01 

0.02 / 0.01 

0.00 / 0.00 

0.01 / 0.01 

0.01 / 0.00 

0.02 / 0.01 

0.04 / 0.02 

0.00 / 0.00 

0.01 / 0.00 

0.04 / 0.02 

0.03 / 0.01 

0.02 / 0.00 

0.01 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.04 / 0.02 

0.01 / 0.00 

0.00 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.01 / 0.00 

0.06 / 0.01 

0.01 / 0.00 

0.02 / 0.01 

0.03 / 0.00 

0.05 / 0.01 

0.01 / 0.01 

0.02 / 0.02 

0.01 / 0.01 

0.00 / 0.00 

0.00 / 0.00 

0.02 / 0.02 

0.00 / 0.00 

0.01 / 0.01 

0.06 / 0.05 

0.09 / 0.04 

0.03 / 0.02 

0.04 / 0.01 

0.45 / 0.19 

0.22 / 0.07 

0.69 / 0.45 

0.14 / 0.02 

0.24 / 0.06 

0.09 / 0.01 

0.01 / 0.00 

0.02 / 0.01 

0.01 / 0.01 

0.01 / 0.01 

0.01 / 0.01 

0.01 / 0.00 

0.02 / 0.02 

0.02 / 0.01 

0.00 / 0.00 

0.02 / 0.00 

0.01 / 0.00 

0.08 / 0.01 

0.03 / 0.01 

0.00 / 0.00 

0.03 / 0.01 

0.01 / 0.00 

0.06 / 0.02 

0.04 / 0.02 

0.01 / 0.00 

0.01 / 0.00 

0.03 / 0.01 

0.02 / 0.01 

0.04 / 0.00 

0.01 / 0.01 

0.00 / 0.00 

0.01 / 0.00 

0.01 / 0.00 

0.02 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.01 / 0.01 

0.03 / 0.02 

0.01 / 0.00 

0.00 / 0.00 

0.01 / 0.00 

0.02 / 0.01 

0.01 / 0.00 

0.06 / 0.01 

0.01 / 0.00 

0.03 / 0.01 

0.02 / 0.00 

0.02 / 0.01 

0.03 / 0.04 

0.04 / 0.05 

0.02 / 0.02 

0.02 / 0.02 

0.01 / 0.01 

0.04 / 0.03 

0.01 / 0.01 

0.03 / 0.04 

0.04 / 0.03 

0.05 / 0.02