Investigation of Selective Editing Procedures for the Annual Survey 

of Government Finances 

Loretta A. McKenzie, Terri L. Craig, Carma R. Hogue 

Office of Statistical Methods and Research for Economic Programs, 

U.S. Census Bureau,1 Washington, DC 20233-9100 

Abstract 

The U.S. Census Bureau re-engineered the edit procedures for the Annual Survey of Government Finances. Beginning 

with the 2004 survey, the Census Bureau investigated the use of selective editing. A score was assigned to each 

individual government unit based on the effect that a change in the government’s reported data would have on the final 

estimates. Using predetermined critical score values, those government units with the largest potential impact on the 

estimates were pinpointed as candidates for manual review. Because the survey form covers a large number of variables, 

some of which are very volatile from one year to the next, determining which variables to use to develop a score for a 

governmental unit was problematic. We researched several score functions involving different combinations of these 

variables. For each score function we set edit acceptance rates and compared absolute pseudo-biases of resulting 

estimates to identify edit acceptance rates that would reduce edit burden and maintain quality. This paper reviews the 

selective editing technique, the edit research process, and the problems that we encountered in attempting to apply it to 

the Annual Survey of Government Finances. We used empirical research methods to conclude that other micro-editing 

techniques should be used prior to selective editing. Data sources were the 2002 Census of Governments Finances and 

the 2004 Annual Survey of Government Finances. 

Keywords: selective editing, score function, edit referral rate, edit acceptance rate 

1. Introduction 

In an effort to improve the estimates for the Annual Survey of Government Finances (ASGF) while optimizing costs, 

a team of analysts and mathematical statisticians examined various editing methodologies. Analysts were spending much 

of their time making data corrections that had little or no impact on the final estimates. Their valuable time could be 

spent in other survey-related activities, like non-respondent follow-up activities. 

The team examined the consistency edits to determine if they were overlapping or totally covered by other consistency 

or ratio edits. After unduplicating edits, the team examined various methods (Hidiroglou-Berthelot, resistant fences, 

asymmetric fences) of calculating the ratio edit bounds. The team also examined the possible use of selective editing. 

In this paper, Section 2 gives the background of the survey. Section 3 is a review of the edit research process. Section 

4 covers the selective editing methodology. Section 5 covers our case study. Sections 6 and 7 cover the results and 

conclusions, respectively. Section 8 covers future research. 

2. Background 

The ASGF publishes detailed financial data for revenue, expenditures, assets, and debt from state and local governments 

(cities, counties, townships, special districts, and independent school districts) each year. The survey mails out in 

October and requests data for the government’s fiscal year ending between July 1 of the previous year and June 30 of 

the survey year. 

In years ending in ‘2’ and ‘7’ , a census of all state and local governments is done. In all other years, a sample of cities, 

counties, townships, and special districts is surveyed. All independent school districts (about 14,000) and state 

governments are surveyed every year. The total sample size including all types of government (states, cities, counties, 

towns, special districts, and independent school districts) is about 25,000. 

1

This report is released to inform interested parties of research and to encourage discussion. The views 

expressed on statistical, methodological, technical, or operational issues are those of the authors and not necessarily 

those of the U.S. Census Bureau. 
 

 

 

 

 

 

 

Cities, counties, and townships are called general purpose governments since they perform multiple functions (fire 

protection, highway administration, welfare, police protection, etc.). Special district governments are fiscally and 

administratively independent entities that generally have only one purpose. These governments include airport 

authorities, sewer districts, utilities, etc. Each special district is categorized by a function code that indicates its function. 

There are some special districts that have more than one function, like sewer and water supply or natural resources and 

water supply. These are referred to as multi-function special districts. All other special districts are single-function. 

The items collected on the questionnaire are the same for census and sample years. Data on several hundred variables 

are provided by state and local governments. The variables include various taxes, charges, assessments, 

intergovernmental revenue, utility revenue, liquor store revenue, and insurance trust revenue. Capital outlay and current 

operation expenditures are collected for various functions (governmental administration, utilities, safety, transportation, 

etc.). Long-term and short-term debts, as well as data on various cash and security holdings, are also included in the data 

collection. For more information on the survey, go to http://www.census.gov/govs/www/financegen.html. For 

information on the sample design, see “Sampling Procedure for the Annual Survey of Government Finances” by 

McLaughlin and McKenzie (2006) which is available upon request. 

For survey year 2005, all of the questionnaires used to collect the data were redesigned. With a major redesign of the 

questions, we also needed to redesign the edit system. The aforementioned team was formed to overhaul the existing 

edit system and to research the best methods for detecting edit failures. 

3. Edit Research 

The goal of survey editing has been for the analyst to isolate and correct as much of the non-sampling error as possible. 

In the past few years, survey methodologists in government agencies have been researching how to reduce survey costs, 

produce estimates more efficiently, and reduce respondent burden all at the same time. Researchers have been 

questioning the value of this extensive editing. If the effort to validate the data does not noticeably affect the final 

tabulated results, the cost is not justified. Current edit procedures for the ASGF consist of a thorough micro-editing of 

each survey unit. Micro-editing, which is very costly, finds errors by inspecting individual units. 

Several edit methodologies can be used that could potentially increase the efficiency of the ASGF edit process. For the 

ASGF we researched three edit methodologies: the Hidiroglou and Berthelot (HB) edit parameter determination, the 

resistant fences (RF) methods of determining parameter bounds, and selective editing to determine units that should be 

reviewed. We analyzed the effectiveness of each method to determine which method would best suit the ASGF data. 

Our research is explained in more detail in Cornett, McLaughlin, and Hogue (2006). This paper looks at the HB and 

RF methods of determining edit bounds. 

Selective editing is designed to identify those possible erroneous units that have the most impact on the survey estimates. 

It identifies a whole unit for review, but all of the variables on the questionable record must be reviewed. Selective 

editing does not isolate erroneous variables for questionable records. Since the ASGF has numerous variables for review, 

selective editing was researched to see if it could be used as an editing tool to prioritize previously identified edit failures 

for edit follow-up. 

4. Selective Editing Methodology 

The edited micro-data is passed to a macro-editing process, which detects errors at some aggregate level based on the 

responses of a number of units. Selective editing can help connect the two by focusing on editing an individual unit 

response, but prioritizing error resolution by determining how much the error potentially impacts the survey estimates. 

The goal is to improve quality by targeting editing resources on the responses that have the greatest effect on the survey 

estimates. Selective editing can then rank survey units by the importance that their possible error affects the survey 

outcome. It is a tool that helps move valuable resources away from cleaning unimportant errors to improving other parts 

of the survey process, such as additional macro-editing. 

Selective editing scores each individual government unit on its possible effect on the estimates, and determines critical 

values used to isolate the units with the largest potential impact on the estimates as candidates for manual review. Every 

unit is allocated a score that measures the relative importance of resolving its error in comparison to the other survey 
 

 

 

 

 

 

 

 

 

 

 

 

 

responses. Therefore, this procedure targets only a small part of the micro-data for the analyst to re-contact and validate. 

Surveys that require clean micro-data for all units should not use this method. Selective editing is useful when the reason 

to collect the data is to produce estimates rather than micro-data. 

Using the simulation approach detailed and described in Section 5 by Lawrence and McKenzie (2000), we investigated 

the effectiveness of selective editing using several score functions. This approach evaluates, in terms of the final survey 

estimates, several editing models. In particular, it focuses on each model’s effectiveness in isolating units for review. 

Expected amended values are estimates of an item response; they are not necessarily the correct or final values. Expected 

amended values are calculated for the raw (unedited, reported) data. Scarrott (2005) says these values can be based on 

historical data or responses from similar units. A local score for each survey item of interest is determined. The local 

score is defined to be the expected change for a particular variable that would come about if the raw data were amended 

(i.e., not the final data after editing). Fatal edits, such as blank required items, often are resolved prior to selective 

editing. A single measurement, called a global score, is calculated for each survey unit. A global score is a combination 

of a unit’s local scores. Cell specific critical values, or cut-off scores, are computed using a simulation study and the 

global scores. Examples of cells are size-class categories, type of government, or industry. The distribution of the global 

scores is used to determine the cell specific critical values. The unit’s global score is compared to its cell specific critical 

value. A unit with a global score greater than its cell specific critical value is selected for manual review, otherwise the 

data are accepted. Follow-up attention is then given to the survey returns with the highest global scores. 

Research has shown selective editing to be an effective method of reducing survey editing without affecting the quality 

of the final survey estimates. Latouche and Berthelot (1992) found that for a business survey of 987 questionnaires, 40% 

can be computer edited, 20% re-contacted (based on the selective editing score), and 40% left uncorrected, and the 

simulated survey estimates will be within 0.1% of the final estimates for frequently reported variables. Latouche and 

Berthelot (1992) also show selective editing to be effective for continuous variables with skewed error distributions. 

In skewed error distributions, a small number of units have error that is so large in magnitude that it impacts the survey 

estimates. When Thompson and Hostetter (2000) performed a feasibility study for U.S. Census Bureau economic 

programs, they found a local and global score function that worked well for two sequential years of the Annual Survey 

of Manufacturers. Lawrence and McDavitt (1994) showed significance editing reduced respondent burden and recontact, while also providing an ordering so that the most important edit queries could be addressed first. Selective 

editing can be implemented in batches when the data are received, since most implementations only require a unit’s 

current period and prior period responses. This is important for time critical applications. 

5. Case Study 

Our study used raw and edited data from the 2002 Census of Government Finances and the 2004 ASGF sample weights. 

The 2002 Finance Census raw and edited data files and the 2004 sample unit file were used to compute the local and 

global scores. We only used the units from the 2002 Census that were in the 2004 sample. The data file created from 

the 2002 edited file with the 2004 sample weights was considered to be the expected amended values. 

The survey form covers a large number of variables, including governmental functions, and some are very volatile. 

Determining which variables to use to develop a global score for a governmental unit was problematic. Several global 

scores were made using different categories of variables. They were compared in terms of absolute pseudo-bias (APB) 

and acceptance rate. The acceptance rate is 100 times the number of cases accepted as reported divided by the number 

of sample cases. One score contained five fundamental aggregate variables, including all revenue and expenditure 

variables at a high aggregate level. Other scores considered different aggregate variables to specifically research 

volatility; others included more variables in the global score. 

The survey units were ranked by the global score. We chose several arbitrary values of percentile p, where p denotes 

the editing workload. A smaller p means a larger editing workload, resolving more cases. For example at the 45th 

percentile, 55% of the units are referred for review. Critical values were computed for each global score and value of 

p. This procedure was done for p=45, 55, 65, 75, and 85. Critical values were set in accordance with the amount of 

editing that will be performed. A unit was flagged for review if its global score was above the critical value or cut-off.

 The units with the highest global score were selected for editing. A composite survey returns file was created, for each 

value of p, containing the edited 2002 ASGF data for the top (100- p)% of governments and the raw 2002 ASGF data 

for all other governments (p%). We re-computed the estimates using each composite file. 
 

 

 

Thompson and Hostetter (2000) discuss fatal versus nonfatal edit failures. Examples of fatal referrals include blank items 

and misclassifications which should always be resolved by imputation or analyst investigation. Thompson and Hostetter 

(2000), define z to be a 0/1 indicator for edit failures. An item is said to fail an edit if the reported value =/ edited value j

for non blank reported cases, then z is set to one. Otherwise, z is set to zero. LaTouche and Berthelot (1992) also set j j

z = 1 if it is labeled as suspicious, otherwise it is 0. The ASGF does not have an “in-between” file that identifies the edit 

failures. We do not currently flag cases that fail edits but are verified to be good data. Since the data for many of the 

j

edit failures are not changed, it was difficult to distinguish failures. Therefore, we set all z = 1. j 

We compared all the different score functions in terms of the APB and acceptance rate. The APB due to editing only 

select units was defined to be: 

, 

where: is the estimate of the data item calculated by replacing all unedited reported values where a 

global score function was larger than the critical value with their edited values and leaving the rest 

of the unedited reported values unchanged. 

is the estimate of the data item calculated by replacing all of the unedited, reported values 

with edited values. 

We considered an APB of less than 10% due to not editing to be acceptable. 

5.1 Defining the Local and Global Score Functions 

Reviewing past literature shows most global scores are comprised of four to five local scores. Latouche and Berthelot 

(1992) suggests considering four major elements when developing score functions. These elements are the size of the 

responding unit, the size and number of suspicious data items, and the relative importance of variables as determined 

by the subject matter specialists. We used the following categories, c, of aggregate variables for global scores. 

Category A: non-trust revenue, general expenditures, total taxes, total charges, current operations expenditures 

Category B: non-trust revenue, general expenditures, total taxes, total charges, highway expenditures, health and 

hospital expenditures, criminal justice expenditures, housing expenditures, welfare expenditures, 

utilities expenditures 

Category C: total long term debt, total capital outlay, intergovernmental revenue, intergovernmental expenditures 

Category D: non-trust revenue, general expenditures, total long term debt, total assets 

Category A scores consist of aggregate revenue and expenditure variables. Category B scores consist of 10 of the more 

important detail aggregate variables. We felt this could help reduce the APB, since more detail about the governmental 

unit was being included in its global score. Category C scores consist of highly volatile variables. Category D contains 

variables that were to be collected during a third non-response follow-up. We expected to have these variables for total 

and partial respondents. 

A local score was computed for each aggregate variable included in any global score. For every unit and each of the 16 

unique variables above, we calculated the local score two ways. LS will refer to local score and GS will refer to global 

score. The first local score is the scaled absolute difference between the edited 2002 data and unedited 2002 data (LS 

Equation 1). For the second local score, we calculated the scaled square root of the maximum of edited 2002 data and 

unedited 2002 data (LS Equation 2). The former will be known as DIFF, and the latter known as SQRT. 
 

 

 

 

 

 

 

 

LS Equation 1 (DIFF): LS =

1ij 

LS Equation 2 (SQRT): LS2ij = 

and 

, 

where: = 1,... ,16 (the variable of interest),

 = 1,... , n (the unit or ID of interest), 

is the 2002 amended data for variable i, unit j, 

is the 2002 reported data for variable i, unit j, 

= 1 for edit failures and 0 otherwise, and 

= weight for unit j. 

Dividing by the absolute value, to scale the local score, compensates for variables that have a different order of 

magnitude. SQRT was found promising by Thompson and Hostetter (2000). DIFF emphasizes the absolute discrepancy 

between the reported and amended data and is suggested in Lawrence and McKenzie (2000). 

We chose two methods to calculate the global score from each type of local score. We used the maximum local score 

for each unit (GS Equation 1) and the sum of the local scores for each unit (GS Equation 2). The former will be called 

MAX and the latter SUM. MAX was used by Lawrence and McDavitt (1994) and gives attention to a substantial error 

in any one variable. 

GS Equation 1 (MAX): GS =

 1kc j . , i 0 c, and 

GS Equation 2 (SUM): GS 2kc j . = , i 0 c, 

where: k = 1,2 (the local score), 

c = A, B, C, or D (the category), 

i = 1,... ,16 (the variable of interest), and 

j = 1,... , n (the unit or ID of interest). 

We had four categories, two types of local scores, and two types of global scores, so 16 global scores were computed 

per unit. We prepared 16 sets of estimates, using the composite files, to compare in terms of APB for each value of p. 

6. Results 

General purpose governments were researched separately from special districts and supplements. General purpose 

governments, which perform multiple governmental functions, can have over a hundred variables because of the myriad 

of functions they perform. On the other hand, a special district government usually has one function such as an airport 

authority, so there are not as many reported variables. A supplement supplies additional data for a general purpose 

government. Examples would be hospitals that would supply expenditures, revenues, etc. Therefore, a supplement is 

much like a special district in the amount of data that it reports. For general purpose governments, cut-off scores (critical 

values) were set at the state by type of government level. However, due to time and resource constraints, we analyzed 

the composite estimates at the national by type of government level. For special district governments, cut-off scores 

were set and the composite estimates were analyzed at the function level. This analysis was as detailed as we could get 

for special districts, because of the scantiness of the data for some functions in the sample. 

6.1 General Purpose Governments 
 

 

 

 

 

 

 

 

 

 

 

We considered using selective editing as an editing tool by itself for general purpose governments for fiscal year 2005. 

There are 16 variables of interest that we analyzed at the estimate level for each global score. Forty-eight states have 

counties, 49 have cities, and 20 states have towns. We looked at 5 values of acceptance rates (for p = 45,55,65,75,85). 

We computed an APB for each type of government, state, variable, global score, and acceptance rate. So, for counties 

there were 768 (48 x 16) APB calculations per global score and acceptance rate. There were 784 APB calculations for 

cities, and 320 for towns. 

We found for all levels and for all types of general purpose governments, category B (having the largest number of 

variables), produced the greatest number of score calculations with an APB less than 10%. Global scores computed 

using category B almost always ranked in the top two of 16 global scores. Using a score with more variables (category 

B) reduced the APB. The global score calculated using the sum of the local scores tended to work better than the 

maximum of the local scores. Using the scaled difference local score worked better than the scaled square root of the 

maximum. The SUM and DIFF scores gave more APB calculations less than 10% for almost every value of p and type 

of government. Table 1 shows the top performing score by type of government and acceptance rate. All scores in the 

table use category B since it always performed the best. 

Table 1: Top Score by Type of Government for General Purpose Governments. All global scores use category B. 

Type p 

(% of cases accepted as reported) 

Top Global 

Score 

Top Local 

Score 

Percent of APB 

calculations < 

10% 

Percent Change of APB 

calculations < 10% from 

next value of p 

Counties 45 SUM DIFF 81.7% 5.97% 

55 SUM DIFF 77.1 % 7.83% 

65 SUM DIFF 71.5% 13.13% 

75 MAX DIFF 63.2% 17.25% 

85 SUM DIFF 53.9% 

Cities 45 SUM DIFF 94.6% 5.00% 

55 SUM DIFF 90.1% 5.26% 

65 SUM DIFF 85.6% 9.18% 

75 SUM DIFF 78.4% 19.15% 

85 SUM DIFF 65.8% 

Towns 45 SUM DIFF 95.9% 3.68% 

55 MAX DIFF 92.5% 2.77% 

65 MAX DIFF 90.0% 7.91% 

75 SUM DIFF 83.4% 13.62% 

85 SUM DIFF 73.4% 

As additional analyst burden is added, the percentage of APB calculations for the top score less than 10% increases. 

As discussed in Lawrence and McDavitt (1994), the key is to find that point at which continuing to query referrals will 

not substantially increase the accuracy of the final estimates. We proposed a 45% acceptance rate for counties and cities 

and a 55% acceptance rate for towns. When we looked at general purpose governments using 45% raw data and 55% 

edited data, for counties, the applicable variables had an APB less than 10% in 8 out of 10 cases. Cities (p=45) and 

towns (p=55) had better than 9 out of 10 cases with an APB less than 10%. 

Since there were a lot of variables for general purpose governments, we found that it was difficult for an analyst to isolate 

what items to validate from a failed unit. Selective editing targets units for further review that exceed the critical value, 

but does not isolate the variables that should be checked. For this case selective editing would not really reduce the

editing workload unless some other method was used to isolate the problem variables. 
 

 

 

 

 

 

We next researched selective editing as a way to prioritize the ratio edit failures. Currently analysts are given all the 

ratio edit failures to review. When isolating the units that would have the most effect on the final estimates, we 

ranked the ratio edit failures on the amount of change that would result from resolving units with an edit referral. 

Only units that failed a ratio edit would be candidates for selective editing. The variable totals became those from all 

the units with a ratio edit failure. We re-evaluated all scores. Since all failed ratio edits are given back to the analyst 

for review, the value of p was not important. We set p=45 to determine the critical values. We didn’t have enough 

cases to determine critical values at the state by type of government level so we used a grouping of states by type of 

government as determined by the subject matter specialists. See Attachment 1 for the state groupings used. Scores 

were again evaluated at the national by type of government level. For counties there were 12 state groupings, 13 for 

cities, and 6 for towns. There were, respectively by type of government, 192, 208, and 96 possible APB calculations 

per global score. Table 2 summarizes the results. 

Table 2: Selective Editing Results for General Purpose Government Ratio Edit Failures, p=45 

Counties Cities Towns 

Category GS LS Percent 

APB < 

10% 

Category GS LS Percent 

APB < 

10% 

Category GS LS Percent 

APB < 

10% 

B MAX DIFF 96.88 % A MAX DIFF 98.08 % B MAX DIFF 94.79 % 

D SUM DIFF 96.88 % A SUM DIFF 98.08 % B SUM DIFF 94.79% 

A SUM DIFF 96.35 % B SUM DIFF 97.60 % C SUM SQRT 92.70% 

D MAX DIFF 96.35 % B MAX DIFF 97.12 % D SUM DIFF 92.70% 

A MAX DIFF 95.31 % D MAX DIFF 96.63 % B MAX SQRT 91.67% 

D MAX SQRT 95.31 % D SUM DIFF 96.63 % B SUM SQRT 91.67% 

B SUM DIFF 94.27 % C SUM DIFF 96.15% D MAX DIFF 91.67% 

D SUM SQRT 94.27 % C MAX DIFF 94.23% A MAX DIFF 90.63% 

C MAX DIFF 93.23 % D MAX SQRT 93.75% A SUM DIFF 90.63% 

C SUM DIFF 93.23 % D SUM SQRT 93.75% C MAX SQRT 90.63% 

A MAX SQRT 91.15 % A SUM SQRT 92.30% A MAX SQRT 89.58% 

A SUM SQRT 90.62% B SUM SQRT 92.30% C MAX DIFF 89.58% 

B SUM SQRT 90.62 % B MAX SQRT 91.83% C SUM DIFF 89.58% 

B MAX SQRT 90.10 % C MAX SQRT 90.38% D MAX SQRT 89.58% 

C SUM SQRT 86.46 % C SUM SQRT 90.38% D SUM SQRT 89.58% 

C MAX SQRT 85.42 % A MAX SQRT 89.90% A SUM SQRT 88.54% 

For counties, we chose the category B score using MAX and DIFF. Although the SUM, DIFF for category D score 

was tied with the same number of APB calculations less than 10%, we selected category B, MAX and DIFF because 

the category B score had a slightly less problematic APB. Both scores had a high APB for utilities for state group 

10 (100%). Variable IG (intergovernmental) expenditures did not fare well with our top scores for counties, but it is 

not used in any of our current consistency ratio edits. When looking at all scores together, the category B, A, and D 

scores performed better than category C, as expected. DIFF worked better than SQRT. MAX and SUM performed 

equally well. 

For cities, two category A scores gave the highest number of APB calculations less than 10%. DIFF was better than 

SQRT, but MAX and SUM worked equally well. Two of the category B scores were close to the top score. 
 

 

 

 

 

Notably, these scores had high APBs, for group 5 states, for total long term debt (13.86%) and total capital outlay 

(15.33%). 

For towns we also chose the category B score using MAX and DIFF. Categories C and D scores performed 

similarly. The score using more variables appeared to help towns more than counties and cities. All four category B 

global scores were in the top six global scores for towns. 

When prioritizing the ratio edit failures for general purpose governments, MAX was the better method to use for 

determining the global score, but when using all the data SUM was better. DIFF was always better than MAX for 

determining the local score. For general purpose governments category B produced the most APB calculations less 

than 10%, except for cities when only using the ratio edit failures, where category A was the best. 

6.2 Special Districts 

Since special district and supplements report on far fewer variables, it is easier to isolate variables to query for a failed 

unit. For each function code, we researched how each applicable variable performed amongst the 16 global scores. 

Setting p=85 (i.e., 85% raw and 15% edited), we found we could choose local and global scores in such a way that at 

the function level, most variables had an APB less than 10%. The most promising score used category A, the DIFF 

method for determining the local score, and the MAX method for calculating the global score. Unlike general purpose 

governments, it was rarely better to use category B with the larger number of variables. 

We still had problematic variables and functions. The scores and functions where the category A, MAX, and DIFF did 

not perform the best are detailed in Table 3. For example, solid waste management had the most APB calculations less 

than 10% using category B, MAX, and DIFF. We sometimes found the APB for known volatile variables such as capital 

outlay was consistently high. Using the top score for solid waste management(B, MAX, and DIFF), there was still a high 

APB of 18.23% for the variable total capital outlay. For Other Single-Function Districts, two scores tied having the most 

APB calculations less than 10% using category B and MAX. Definitions for all function codes can be found at the link: 

http://www.census.gov/govs/www/class_ch4.html#s4.5. 

Table 3: Functions where Selective Editing performed differently for Special Districts 

Function Category Top Global Score Top Local Score 

Highways D MAX DIFF 

Sewerage B MAX DIFF 

Solid Waste Mgmt. B MAX DIFF 

Other SingleFunction Special 

Districts 

B 

B 

MAX 

MAX 

SQRT 

DIFF 

For certain functions, such as water supply utility, we could refer less than 15% of the data and have all variables with 

an acceptable APB less than 10% using category A, DIFF, and MAX. For special districts and supplements, our 

composite estimates having 85% raw data and 15% edited data produced acceptable APBs. 

7. Conclusions 

Selective editing seems more promising for surveys with a smaller number of variables. For surveys with a large number 

of variables, selective editing may require referring more cases to produce acceptable APBs. 

We found that for general purpose governments, which have much longer questionnaires, using a longer score, such as 

category B, helps the APB. Even though selective editing was not a practical tool by itself, the subject matter analysts 

could use selective editing to review the historical ratio edits ranked by their global score. Selective editing indicated 

which ratio edit failures were the most important to validate, meaning they would most likely have the greatest effect on 

the estimates. Analysts felt it would be a promising tool to help them prioritize their work, especially during a census 

year when there is a heavier workload. 
 

 

We found that for most special district government functions, referring less than 20% of the data yielded an APB less 

than 10% for most applicable variables. Selective editing by itself could be a valuable tool for reducing costs and analyst 

burden for special district governments. 

8. Further Research 

We recommend analyzing global scores at the state group and function level. State groups may behave differently when 

analyzed separately. In the future, we plan to compare the units isolated using selective editing with the ratio edit failures 

for special districts. 

Acknowledgements 

The authors would sincerely like to thank Rita Petroni, Joanna Fane McLaughlin, Yang Cheng, and Mary Christine 

Davies, for their assistance in reviewing this abstract and paper. The authors would also like to thank Steve Owens, 

Aaron Gray, Stan Batton, and Elizabeth Cornett for their participation on this project. 

References 

Cornett, E., McLaughlin, J. F., and Hogue, C. (2006). “A Comparison of Two Ratio Edit Methods for the Annual 

Survey of Government Finances.” Proceedings of the Joint Statistical Meetings, 6-10 August, Seattle. 

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, Vol. 8, pp.389-400. 

Lawrence, D., and McDavitt, C. (1994). “Significance Editing in the Australian Survey of Average Weekly 

Earnings.” Journal of Official Statistics, Vol. 10, pp. 437- 447. 

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

Statistics, 16, pp. 243-255. 

McLaughlin, J.F., and McKenzie, L. (2006). “The Sampling Procedure for the Annual Survey of Government 

Finances.” Internal Document. 

Scarrott, C. (2005). “Feasibility Study: A Review of Selective Editing.” Technical Report, University of 

Canterbury, New Zealand. 

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, 17-

21 June, Buffalo, NY. 
Attachment 1 

State and Type Groups for Editing 

For counties (type 1) the following groups were used: 

Group 1: Alabama, Arkansas, and Mississippi 

Group 2: Alaska, Maryland, North Carolina, Tennessee, and Virginia 

Group 3: Arizona, Nevada, Oklahoma, Texas and New Mexico 

Group 4: Colorado, Idaho, Montana, Utah, and Wyoming 

Group 5: Delaware, New Jersey, New York, and Pennsylvania 

Group 6: Florida, Georgia, Louisiana, and South Carolina 

Group 7: Illinois, Indiana, Iowa, Michigan, Minnesota, and Wisconsin 

Group 8: Kentucky, Ohio, and West Virginia 

Group 9: Maine, Massachusetts, New Hampshire, and Vermont 

Group 10: Hawaii, Oregon, and Washington 

Group 11: North Dakota, and South Dakota 

Group 12: Kansas, Missouri, and Nebraska 

For municipalities (type 2) the following groups were used: 

Group 1: Alabama, Arkansas, and Mississippi 

Group 2: Alaska, Tennessee, and Virginia 

Group 3: Arizona, Nevada, Oklahoma, Texas and New Mexico 

Group 4: Colorado, Idaho, Montana, Utah, and Wyoming 

Group 5: Delaware, New Jersey, New York, and Pennsylvania 

Group 6: Florida, Georgia, Louisiana, and South Carolina 

Group 7: Illinois, Indiana, Michigan, Minnesota, and Wisconsin 

Group 8: Kentucky, Ohio, and West Virginia 

Group 9: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont 

Group 10: Oregon, and Washington 

Group 11: North Dakota, and South Dakota 

Group 12: Iowa, Kansas, Missouri, and Nebraska 

Group 13: Maryland, and North Carolina 

For townships (type 3) the following groups were used: 

Group 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont 

Group 2: Illinois, Indiana 

Group 3: Pennsylvania, New Jersey, and New York 

Group 4: Kansas, Missouri, and Nebraska 

Group 5: North Dakota, and South Dakota 

Group 6: Michigan, Minnesota, Ohio, and Wisconsin