Four-Digits or No-Digit Social Security Numbers -- Impact on the National 

Agricultural Statistics Service Record Linkage Maintenance Processes 

Denise A. Abreu, Kara Daniel, Bill Iwig, Stan Hoge 

National Agricultural Statistics Service 

1400 Independence Ave, SW Washington DC 20250, denise_abreu@nass.usda.gov 

Key Words: Social Security Numbers, personally identifiable information1 (PII), list frame, record linkage 

Abstract 

The National Agricultural Statistics Service (NASS) relies on Social Security Numbers (SSNs) and Employer Identification 

Numbers (EINs) as important matching variables for use in record linkage processing and other list maintenance activities that 

are conducted in order to maintain a high quality list of U.S. farmers and ranchers. Record linkage is used to match the NASS 

list frame to new lists and administrative data sources in order to identify new farmers and ranchers not previously identified by 

NASS. Record linkage is also used to match the list frame to administrative sources for maintenance purposes, for example 

updating telephone numbers on the list frame. However, maintaining nine digit SSNs/EINs on the list frame is a PII security 

concern for NASS. This research evaluated the potential impact of eliminating SSNs/EINs from the list frame or of only 

maintaining four digit SSNs/EINs on the various list building and maintenance record linkage processes. Overall, the results 

showed that using four digit SSNs/EINs for record linkage would result in missing 1 to 2 percent of the actual SSN matches 

and 4 to 8 percent of the actual EIN matches. However, having no SSNs/EINs for record linkage would result in missing 4 to 6 

percent of the actual SSN matches and 9 to 13 percent of the EIN matches. In general, the percentage of missed matches will 

increase as the size of the data sets being matched increases. Since NASS processes several record linkage projects per state per 

year, the cumulative effect of either approach on the quality of the list frame is a concern. 

I. Introduction 

The National Agricultural Statistics Service (NASS) spends considerable effort safeguarding both respondent and employee 

personally identifiable information (PII). In a 2007 memorandum sent to all employees regarding PII, NASS‟ former 

administrator, Ronald R. Bosecker, wrote “It is critically important for all NASS employees and contractors to understand the 

definition of PII, recognize PII when they encounter it, and understand their responsibility for safeguarding it.” Furthermore, 

he emphasized the agency‟s incredible track record in handling PII. He wrote “We have been proud throughout our history 

that we protect the confidentiality of all information relating to those who entrust us with their data. It is the responsibility of 

each employee and contractor, regardless of grade, job series, or location throughout the country.” It is very clear in his 

message that safeguarding PII is not only important to NASS‟ employees but also a major focal point of its upper management 

team. The agency complies with all Federal Information Security Management Act (FISMA) regulations in securing all its 

Information Technology (IT) systems. As required by FISMA, NASS conducts Privacy Impact Assessments (PIA) to evaluate 

how the agency processes, handles, and stores privacy information. As required by the Privacy Act of 1974, NASS also 

published System of Record Notices (SORN) on the Federal Register. These publicly available notices document how NASS 

uses, stores, retrieves, etc. public records that are under its control. Both PIA and SORN help NASS senior management assess 

its security posture in safeguarding public information that are entrusted to NASS. 

The ability to request PII from respondents has been greatly jeopardized due to numerous reports of loss of such information by 

other government agencies in the past few years. These highly scrutinized events raised a great deal of controversy and the 

public‟s trust in the government‟s ability to safeguard their information was weakened. In an attempt to restore the public‟s 

 

1

 The term “personally identifiable information” refers to the information which can be used to distinguish or trace an 

individual‟s identity, such as their name, social security number, biometric records, etc. either alone, or when combined with 

other personal or identifying information which is linked or linkable to a specific individual, such as date and place of birth, 

mother‟s maiden name, etc. 
trust, the Office of Management and Budget (OMB) began a government wide awareness initiative on the collection and 

safekeeping of PII. Additionally, OMB tightened the rules around the collection of PII. In response, government agencies 

began implementing measures to eliminate or diminish the collection of PII, specifically Social Security Numbers (SSNs) and 

Employer Identification Numbers (EINs). In 2006, NASS voluntarily stopped requesting SSNs and EINs on all its major 

surveys. In addition, NASS has considered completely eliminating SSNs and EINs from their processing systems or only 

maintaining 4-digit SSNs and EINs to help reduce the risk of any inappropriate release of sensitive PII. 

NASS relies on SSNs and EINs as important matching variables for use in record linkage processing and other list maintenance 

activities that are conducted in order to maintain a high quality list of U.S. farmers and ranchers. Record linkage is used to 

match the NASS list frame to new lists and administrative data sources in order to identify new farmers and ranchers not 

previously identified by NASS. Some of these new sources contain SSNs and EINs, which are valuable matching variables. 

Record linkage is also used to match the list frame to administrative sources for maintenance purposes, for example updating 

telephone numbers on the list frame. Other list maintenance activities also utilize SSNs and EINs, such as interactive look-ups 

of records on databases. 

This research report evaluates the potential impact of eliminating SSN/EIN from the list frame or of only maintaining four digit 

SSN/EIN on the various list-building and maintenance record linkage processes and other maintenance activities. 

II. Record Linkage 

NASS began utilizing its current record linkage system over 9 years ago. The NASS system was built using AutoStan and 

AutoMatch (formerly sold by MatchWare Technologies) as its base for the standardization and matching of records. These 

software programs were developed using the probabilistic record linkage techniques proposed by Ivan Fellegi and Alan Sunter 

in their 1969 JASA paper. NASS developed front and back end companion products to assist in setting up record linkage 

match parameters and reviewing results. 

NASS begins a typical record linkage project by obtaining an outside list source. This list is then transformed into a standard 

fixed field ASCII text file. Data in this file are formatted such that they meet the standards of the List Frame. Individual 

names are transformed to signature format, and variable length restrictions are imposed so that the length of fields going into 

record linkage match the length of the corresponding fields on the List Frame. For example, name and address fields are 

limited to 30 characters on the List Frame, so name and address fields for each record from the outside source list are 

reformatted to 30 characters. A list identification number is also generated for each record in the outside source list, allowing 

the processor to easily identify the record before and after the match is run. Each outside source list is further assigned a record 

source code, uniquely identifying that list source. Finally, each outside source record is assigned a status code. Examples of 

the status code are: known farm, potential farm, or non-farm record. The status code is continually updated based on 

information received about the record. 

Before processing a record linkage project, a fixed field ASCII text file is pulled from the NASS List Frame database. The 

layout of this file is identical to the layout of the outside source ASCII file. The List Frame extract typically contains all 

records on the frame, including known farms, potential farms, and non-farms. 

After the outside source and List Frame fixed field ASCII text files are created, a SAS program is run to determine if the cities 

and ZIP Codes from the outside source correspond to the United States Post Office standard cities and ZIP Codes. If a postal 

standard city and ZIP Code cannot be found, a report is generated noting that the city or ZIP Code information is incorrect and 

should be updated before the match is processed. The SAS program also verifies that telephone numbers, SSNs, and EINs 

meet certain validity standards. If they are not valid, a report is generated. Once the verify program is finished and the file is 

free of all errors, a standardization process is run on both the outside source and the List Frame files. 

The standardization process parses the names and addresses into their component parts. For example, a person name could be 

parsed into a prefix, first name, middle name, last name, last name suffix and title. During the standardization process, input 
name and address components are replaced with standard values. This standardization removes the effect of common 

nicknames and spelling variations. It also ensures that like information is compared during the match process. 

NASS utilizes AutoMatch software to match the outside source and List Frame files. For each project, a set of blocking 

variables is used to divide the data into mutually exclusive subgroups. Records with common values for the blocking variables 

are compared during the match process, and records where the blocking variables differ are considered non-matches. For 

example, one pass may block on the ZIP Code. Records with common ZIP Codes are compared during the match process, 

while records with different ZIP Codes are considered non-matches. Multiple passes with different blocking and matching 

variables are run to compensate for inaccuracies in the data. Once the blocks have been determined, values for a series of 

match variables are compared. If the values agree, a positive weight is generally assigned. If the values disagree, a negative 

weight is generally assigned. The weights for each of the variables are then summed up to come up with a composite weight, 

which is a measure of the likelihood that a record pair is a match. 

After a pass is run, a report is generated showing possible links. Linked pairs are sorted according to their composite weights, 

and links are reviewed in SAS. During the review process, two cutoff values, referred to as the upper and lower cutoffs, are set 

for each pass. Any record pairs with a composite weight higher than the upper cutoff are considered matches; record pairs with 

composite weights lower than the lower cutoff value are considered non-matches; and record pairs with weights between the 

two values are considered possible matches. The possible match records must be manually reviewed before a final review 

status will be set. Ideally this review would be done between each pass; however, this would become logistically infeasible 

because review of possible matches is done by personnel in the Field Offices. Rather than review possible matches between 

each pass, NASS combines the results from all passes into one final review using a SAS program. This SAS program brings 

all related records together into groups which NASS refers to as link groups. A link group contains all match pairs involving 

the same outside source or List Frame records. Additionally, all records marked on the List Frame as associated with an 

operation are brought into the group (for example, partner or manager records). Each link group is then classified as either a 

match, possible match, or non-match based on how the pairs that make up the link group were classified during the matching 

process. 

 

NASS developed its own resolution system for reviewing record linkage projects. After an outside list source has been 

matched to the List Frame, a record linkage database is populated with the results. This database is independent of the List 

Frame. However, the resolution system has many features which allow those performing resolution to view and update List 

Frame information as needed. Field Office (FO) staff does the majority of the resolution work. The employees in the FO have 

experience with the agriculture in that State and work closely with important State Agriculture contacts. To resolve the link 

group, FO staff reviews the records and determines the match status. At times, phone calls are made to verify operating 

arrangements. 

When the FO reviews the link groups, the reviewer goes through the link group to determine which List Frame record best 

matches the outside source record. If an outside source record does not best match the first List Frame record in the link group, 

the reviewer can change the link group number so that the outside source record and the best List Frame record are in the same 

link group. The reviewer can create up to 10 new link groups (sub link groups) for a particular existing link group. The NASS 

record linkage system creates a composite record which represents each operation contained in a link group. Once all the 

records are reviewed and each one is grouped with its best List Frame record, a composite record is regenerated for each sub 

link group. These composite records are the ones used in generating any needed transaction files for possible name and 

address, control data, and any other updates. Additionally, the composite records are used to create new add records to be 

posted to the List Frame. Users have the ability to alter information in the composite record so that it represents the operation 

as completely and accurately as possible. See Appendix A for a flowchart of the entire record linkage processing guideline. 

III. Methodology 

Analysis was conducted to evaluate the impact of not using SSNs and EINs and of just using four digit SSNs and EINs on 

record linkage and NASS current list maintenance activities and procedures. The 2007 Farm Service Agency (FSA) data files 

for Arizona (AZ), Montana (MT), South Carolina (SC), South Dakota (SD), and Texas (TX) were used for matching to the list 

frame. 
FSA data are especially valuable for multiple record linkage projects as well as various maintenance activities since most 

records contain full nine digit SSNs or EINs and provide efficient matching results. This affords an easier matching process 

and shorter review time for field office (FO) staff. However, FSA data are also prone to error. One concern with these data is 

that SSNs are not always correct. Family members or other individuals associated with the same operation often use the same 

SSNs when reporting their operations. This can present problems when setting cutoffs and during review. 

The files being matched were processed through multiple passes (blocks/subsets) which did not include any special identifiers 

such as SSNs and/or EINs. These will be identified as the „general passes.‟ The general passes consist of blocks of variables 

using several of the parsed items from the information available for respondents. For example, one pass would consist of a 

data subset based on surname NYSIIS2, first name and middle name. Thus, pairs of records with the same surname NYSIIS, 

first name and middle name will come together as matches. All other pairs will be considered non-matches. Another example 

would be a pass on operation name and city. Twenty-two general passes were created using these criteria. See Appendix B, 

Table B1. 

Since the full nine digit SSN was available for both the FSA and list frame files, FSA records were flagged whenever any FSA 

SSN matched an SSN on the list frame (nine digit to nine digit). This allowed tracking FSA SSNs already on the list frame 

throughout the entire research project. Flagged SSNs in the non-matched group were considered missed SSNs since they 

should have been linked at some point in the match process. In other words, these records should have been possible or 

definite matches instead of non-matches. The same steps undertaken for SSNs were also applied to EINs. 

Four record linkage models were created to assess the impact of not maintaining SSNs and/or EINs on the list frame: a 

standard model (nine digit), a four digit option 1 model, a four digit option 2 model, and a no SSN/EIN model. The standard 

model consisted of the 22 general passes plus 2 additional passes -- one blocking on nine digit SSN and other blocking on nine 

digit EIN. The four digit option 1 model consisted of the 22 general passes in addition to 6 other passes involving the use of 

the four digit SSNs and four digit EINs in combination with several other fields such as last name, first name, operation name, 

etc. The six passes selected for this model yielded similar number of matches when compared to their nine digit counterparts. 

The four digit option 2 model consisted of the 22 general passes plus 2 additional passes -- one blocking on four digit SSN & 

city and the other blocking on four digit EIN & city. The underlying basis for the former model came from an evaluation of 

the SSNs on the list frame. Counts of unique SSNs and four digit SSNs along with other fields (such as last name, county, city, 

etc.) were obtained. The results of the evaluation showed that for 96.2 percent of the list frame records, the combination of the 

four digit SSN, city and state was unique. This strong “uniqueness characteristic” made the combination of four digit SSN/EIN

and city a viable replacement for SSN/EIN in the record linkage processing. Additionally, this model had a small number of 

missed SSNs and EINs relative to the other passes evaluated. The no SSN/EIN model consisted of the 22 general passes, all 

which excluded both SSNs and EINs. Table 1 provides a brief description of each model evaluated. 

Table 1: Four Models Evaluation and their Descriptions 

Model Name Description 

Standard Model (9-digit) 3 General Passes w/ nine digit SSN & nine digit EIN

4-digit Option 1 Model General Passes w/ four digit SSN w/ City, First/Last, Last only and four digit EIN

w/ city, operation name, keyword on operation name 

4-digit Option 2 Model General Passes w/ four digit SSN w/ City and four digit EIN w/ city 

No SSN/EIN Model General Passes w/out SSN/EIN

In general, we accept the nine digit SSN as a unique identifier, and substantial weight is placed on its ability to identify like 

 

2

NYSIIS (New York State Identification and Intelligence System) is a phonetic algorithm for indexing names by their sound 

when pronounced in English. Its basic aim is to encode names with the same pronunciation to the same string so that matching 

can occur despite minor differences in spelling.

3

 General passes refer to the 22 passes which did not involve the use of SSNs and EINs. 
records. One of the strengths of SSN is that we are fairly confident that we have a definite match if SSN and other pieces of 

information agree. Having SSN is a great tool to reduce both HQ staff‟s processing time and FO‟s review time. We are able to 

classify more records as definite matches having SSN than we would be able to otherwise. 

IV. Results 

We evaluated the effect the four digit SSN and four digit EIN had in processing time, review time, “miss rate” of SSN/EIN

matches, and staff time. Table 2 presents the results for each model, for all five states combined. The table provides the 

number of definite and possible matched link groups, number of FSA records that were definite matches to records on the list 

frame, FSA records that did not match any records on the list frame (non-matches); and FSA records that were possible 

matches to list frame records 

Table 2: Number of Definite, Possible and Non-Matched Link Groups4 & Records by Model-Overall 

Model 

Definite 

Matched 

Link 

Groups 

FSA Records 

Definite 

Matches to 

List Frame 

Records 

Possible 

Matched 

Link 

Groups 

FSA Records 

Possible 

Matches to List 

Frame Records 

FSA Records Not 

Matching to List 

Frame 

Records/Link 

5 Groups 

Totals FSA 

Records 

Standard (9-digit) 69,088 87,810 276,431 990,643 978,457 2,056,910 

4-digit Option 1 66,790 85,238 272,661 990,130 981,542 2,056,910 

4-digit Option 2 69,047 87,289 270,582 964,871 1,004,750 2,056,910 

No SSN/EIN 66,326 84,879 274,149 963,373 1,008,658 2,056,910 

The results show that the no SSN/EIN and the four digit option 2 models had a higher number of non-matching FSA records, 

indicating that duplication would be added to the list frame. The no SSN/EIN model would add over 30,000 records as 

duplicates to the list across all five states (1,008,658 - 978,457). The four digit option 2 did not do much better than the no 

SSN/EIN model since it would add over 26,000 duplicates to the list frame. Even though the standard model contained slightly 

larger review workloads, it had a significantly smaller number of non-matches. In other words, the model identified more FSA 

records requiring review, while identifying noticeably fewer records that should have been added to the list frame. The 

compounded effect of adding duplicate records would increase as the number of states processed increases. 

The four digit option 1 model performed about the same as the standard model. As compared to the four digit option 2 and no 

SSN/EIN models, it identified more FSA records that required review and fewer that should be added to the list frame. 

However, this model and the four digit option 2 model consisted of a more complex record linkage structure. The four digit 

option 1 model required additional staff processing time to prepare and to set cutoffs since it had four additional passes. For 

this model, staff had to cycle through more data to achieve reasonable pairs from which to set cutoffs. Cutoffs vary depending 

on the composition of the records on the data files, and a known error rate is assumed when setting them. Staff would allow 

some unreasonable linked pairs to filter through to be able to identify a linked pair which obtained a lower weight than 

expected. It is assumed that these unreasonable records will be identified by FO staff during their review of clerical matches. 

This practice of allowing unreasonable linked pairs to filter through was more prominent in the case of the models involving 

four digit SSNs and EINs (options 1 and 2) than it was for the standard nine digit model. There were often a higher number of 

non-matched FSA records in the cases utilizing four digit or no SSN/EIN matching, which led to missing valid matches. The

alternative matching procedures also often required more data review to get to a good pair of records. Record linkage cutoffs 

were typically higher for these models (compared to cutoffs that staff are accustomed to), since a large number of unreasonable 

pairs would filter in between good pairs, thus increasing the models‟ miss rate. Overall, the predicting power of the four digit 

option 1 model was very close to that of the standard model when minimizing or limiting the amount of duplicate records 

 

4

 A link group may contain multiple FSA and list frame records. Hence, the number of records is greater than the number of 

link groups. 

5

 Non-match link groups only contain one record. Link groups with more than one record are classified as either matches or 

possible matches. 
added to the list frame. 

 

An error rate measure based on nine digit SSN/EIN was obtained whenever any FSA SSN or EIN matched an SSN or EIN on 

the list frame (nine digit to nine digit). This allowed tracking FSA SSNs and EINs already on the list frame throughout the 

entire research project. Within the match, clerical, and non-match FSA records, all matching SSNs and EINs were identified. 

Flagged SSNs and EINs in the non-matched group were considered missed since they should have been linked at some point in 

the matching process. Table 3 presents the number and percentage of FSA SSNs and EINs not linked during the matching 

process for each of the models for all five states combined. The table also illustrates the overall total amount of duplication 

that would be added to the list frame as a result of the SSNs and EINs not linked or missed. 

Overall, the results show that using four digit SSN/EIN option 1 for record linkage would miss 1.8 percent of the actual SSN

matches and 7.2 percent of the actual EIN matches. Furthermore, using this model would add about 1.3 percent duplication to 

the list frame. However, having no SSN/EIN for record linkage would result in missing 5.5 percent of the actual SSN matches 

and 11.9 percent of the EIN matches. Additionally, totally excluding SSN/EIN from record linkage procedures would add over 

3 percent duplication to the list. It is expected that significantly more records would be missed in larger states. 

Table 3: SSNs and EINs not Linked During Matching Process – All States 

Total FSA FSA SSNs Total FSA FSA EINS 

SSNs not not Linked EINs not not Linked Duplication 

Model Linked (%) Linked (%) 6 Duplication (%) 

7

Standard (9-digit) 0 0.0% 2,819 4.5% 2,819 0.3% 

4-digit Option 1 8,449 1.8% 4,490 7.2% 12,939 1.3% 

4-digit Option 2 23,267 5.0% 5,484 8.8% 28,751 2.9% 

No SSN/EIN 25,576 5.5% 7,405 11.9% 32,981 3.3% 

V. Impact on List Frame Maintenance Activities -- Eliminating SSNs/EINs or Using Four Digit SSNs and EINs 

List frame maintenance activities are highly expensive both in human and equipment resources. The NASS record linkage 

system was designed such that lists could be accurately matched to one another while minimizing human resources. 

Furthermore, the system was designed such that additions and updates to the list frame could be made as efficiently and 

effectively as possible. A number of maintenance updates to the list frame are based heavily on the use of SSNs and EINs. 

Headquarter staff have made various changes in processing to incorporate the use of SSNs and EINs during record linkage 

processing such that specific records are excluded from initial review, thus minimizing staff time and maximizing productivity. 

Matching on SSNs and EINs has proven to be very beneficial in achieving these goals. Various list frame maintenance 

activities were evaluated to determine the overall impact of moving to the four digit SSN or not maintaining SSNs at all would 

have on the list frame over time. 

FSA SSN/EIN Updates 

Every year the SSN/EIN Update record linkage projects are conducted. The goal of these projects is to use FSA data to post 

SSN/EIN updates to the list frame. Some of the original FSA data files used to process the FSA SSN/EIN updates for MT and 

SC in 2006 were available for this research. For the purpose of this research, the files were matched to the list frame records, 

and SSNs were flagged and „removed.‟ This provided an idea of how not performing this maintenance activity would impact 

the list frame. The initial run for MT had 53,816 matching SSNs. The number of matching SSNs was reduced to 37,059 after 

removing SSNs that had been updated through the FSA SSN updates. This represents a reduction of 31 percent fewer 

matching SSNs.

 

6

 Duplication = SSNs not linked + EINs not linked 

7

 Not all matches involving nine digit EINs are valid matches since EINs are recycled. 
In the initial run for SC, there were 38,049 matching SSNs, but only 28,568 for the reduced run -- a reduction of 25 percent. It 

is important to note that all the files used for these yearly SSN/EIN update projects were no longer available. This indicates 

that the percentage of SSNs „removed‟ would have been higher. Tables C1, C2, C3 and C4 in Appendix C show the results for 

MT and SC. The four models were processed again and counts were obtained with fewer matching SSNs. The analysis showed 

the impact on record linkage of having fewer records with SSN/EIN on the list frame. For example, in the standard model for 

MT the number of definite matches goes down from 5,595 in Table C1 to 4,434 in Table C2, Appendix C. Also in Appendix 

C, the number of non-matches goes up from 137,223 (Table C1) to 141,425 (Table C2). This demonstrates that having more 

SSNs on the list frame provides more definite matches and fewer non-matches. This is a clear indication of the long-term 

impact of fewer records on the list frame with SSN/EIN values. 

The SSN/EIN update record linkage projects currently give the FOs the option to review records prior to Headquarters 

performing SSN updates. Moving to using four digit SSN, the record review portion for FOs would not be optional, in hopes 

of reducing the error rate added due to the shortening of SSN. Thus, shortening of the SSNs implies more review for states. 

Additionally, FO staff are instructed to review one project, before staff can process another one. Thus, this will create a larger 

backlog of projects for FO personnel. 

 

FSA Missing Phone Match 

For the FSA missing phone match project, record linkage uses SSNs and EINs as its strongest passes. Similar to the SSN/EIN

update projects, definite matches are automatically updated without FO staff reviewing any of the records. Using four digit 

SSNs/EINs would require incorporation of a review for FO staff. The same issues arise regarding error rates and FO review 

backlogs. 

FSA Minority Code Updates 

FSA data are also used to update minority codes on the list records. List frame records with missing minority codes are 

updated by matching the list frame to FSA records with minority codes. The stronger passes involve using SSNs and EINs. 

Eliminating SSNs or using only the four digit SSNs will greatly limit the ability to properly classify records as matches. Even 

though we could still perform this project using names and addresses, the predicting power will not be as strong and thus will 

miss valid matches between FSA and the list frame. The ability to identify minority records on the list frame is important to 

NASS since it helps improve the accuracy of coverage measurements of minority farmers in our surveys and the Census of 

Agriculture. 

NASS Across and Within State Unduplication 

The agency also performs across-state and within state duplication removal efforts using primarily SSNs and EINs. This 

project would have to be performed on a very limited basis. SSN and EIN are the strongest identifiers for uniquely matching 

records for across state duplication. Without nine digit SSN/EIN, the power of any across state duplication record linkage 

would be very limited, primarily because the aim is to find the same person with different addresses in different states. Only 

using four digit SSN/EIN with name would greatly weaken the linkage process. This would especially hold true in this 

situation where the state code is eliminated from the matching process, thus the only field left to match is four digit SSN/EIN. 

Social Security Administration (SSA) Death Match

NASS receives death master files from the Social Security Administration (SSA) on a yearly basis. The only available 

information on SSA files is name, date of birth, date of death, zip code and SSN. These data are used to identify the records of 

deceased operators. This project relies heavily on the use of SSNs as a matching field and thus will have to be eliminated from 

our current maintenance activities. Even though we could potentially match on name and zip code, this would represent a 

massive review effort for FO staff. Matching on these fields is not nearly as effective as matching on SSN. For the SSA death 

match, we are dealing with 69M records, a number significantly higher than the total number of records on the list frame. 

Thus, matching only on name and zip code would increase the margin of error significantly. With this number of records, there 

are more chances of identifying records with the same four digit SSNs than there are on the list frame. It becomes much more 
difficult to discriminate between matched record pairs. Especially since the data fields provided by SSA are much more 

limited. 

The Census of Agriculture mail list (CML) is comprised of all records eligible to receive a census questionnaire. The SSA 

death match project identifies records of deceased operators on the list frame. Based on this project, over 32,000 records 

received a special code indicating that their corresponding operators could be deceased and should be given special attention 

during census data collection. 

Currently, FO staff interactively searches information on Social Security Death Index (SSDI) sites using SSN whenever they 

receive indication that an operator is deceased. Without SSNs available, these searches would not be possible. Additionally, 

the search engines are not designed to search on four digit SSNs and a larger number of returns would be expected since four 

digit SSNs alone are not a strong, unique matching variable. 

Federal Tax Information (FTI) Processing 

During the Federal Tax Information (FTI) processing some records are deemed matches and removed from further processing 

through the use of the SSNs and EINs. This processing step reduces the number of records required for further processing and 

at the same time reduces the review workloads of FO staff. Appendix D provides results for TX, showing the additional 

number of records which would need to be processed as a result of eliminating the SSN passes from the FTI project. The 

appendix provides the number of records that would be processed using nine digit SSN/EIN and four digit SSN/EIN with 

placename. The results show that eliminating the SSN and EIN passes from processing will quadruple the number of records 

requiring review by NASS staff. This also signifies that it will take longer for the project to be processed and FO staff will 

require more time to conduct their review. However, using either nine digit or four digit SSNs/EINs would reduced the 

workload by 1⁄4. For FTI, IRS will not provide only four digits to avoid incorrect identification of respondents. Thus, NASS 

will be receiving nine digits from IRS regardless of what is internally used to match to the list frame. It is important to note 

that safeguard procedures are in place which prohibit IRS information from being used to update the list frame. IRS data are 

used for processing purposes only. 

General Database Search 

We should also expect an increase in staff time since currently staff are proficient with database searches based on nine digit 

SSNs, and it will be much harder to discriminate between different operations with similar data without SSNs. For example, if 

there were two records with differing suffixes (JR/SR) and similar addresses, having SSNs allow reviewers to make a more 

definitive decision than they could without SSNs. Without SSNs, reviewer might call these operators the same; however, with 

the aid of SSNs it will be clear that the two are different. 

VI. Conclusions 

Overall, the results show that using four digit SSNs/EINs for record linkage would result in missing 1 to 2 percent of the actual 

SSN matches and 4 to 8 percent of the actual EIN matches, with a higher miss rate in large states. However, having no 

SSNs/EINs for record linkage would result in missing 4 to 6 percent of the actual SSN matches and 9 to 13 percent of the EIN

matches, with a higher miss rate in large states. NASS processes several record linkage projects per state per year and if we 

add this same level of duplication per project and missed these percentages of SSNs and EINs the compounded effect of using 

four digit would be significantly higher as the years go on. The impact of not using any SSNs/EINs at all would decrease the 

quality of the list frame even more. 

The results show that completely eliminating SSNs and EINs greatly impacts NASS‟ ability to maintain and run smooth 

processes. If SSNs and EINs are completely eliminated from the list frame and all record linkage processing, HQ staff will 

have to process significantly more data to set record linkage parameters. Additionally, FO staff will have a significantly 

increased record linkage review workload. 

VII. Recommendation 
NASS needs to maintain nine digit SSNs/EINs on the list frame for use in record linkage and maintenance activities in order to 

avoid significant erosion of the quality of the list frame over time, including: 

Adding duplication from new list sources; 

Missing drop records from the SSA Death Match; 

Missing minority code updates; and 

Missing removal of duplicate records from within-State and across-State un-duplication projects. 

VIII. References 

Bates, N. (2005). Development and Testing of Informed Consent Questions to Link Survey Data With Administrative Records. 

ASA Proceedings of the 2005 Joint Statistical Meetings. 

Bosecker, R. (2007). “Personally Identifiable Information.” Internal memorandum, National Agricultural Statistics Service. 

November 8, 2007. 

Johnson III, C. (2007). Safeguarding Against and Responding to the Breach of Personally Identifiable Information. 

Memorandum for the Heads of Executive Departments and Agencies. Executive Office of the President- Office of 

Management and Budget. May 22, 2007. M-07-16

Meyer, P.S., Dahlhamer, J.M., and Pleis, J.R. (2006). Developing New Methods and Questions for Improving Response and 

Measurement on Sensitive Questions on the National Health Interview Survey. ASA Proceedings of the 2006 Joint Statistical 

Meetings. 

Nealon, J. (2007). “NASS Plant to Eliminate Use of SSN.” Internal memorandum, National Agricultural Statistics Service. 

Smith, H., Daniel, K., Abreu, D., Hoge, S., and Iwig, W. (2006). Record Linkage and Automatic Maintenance Activities. ASA 

Proceedings of the 2006 Joint Statistical Meetings. 

 

USDA Privacy Impact Assessment (PIA). UNIX MA/ USC Systems. Prepared by DSD Laboratories. March 15, 2007. 
Appendix A 
Appendix B 

 

Table B1: Description of Blocking Variables for 22 General Passes 

Pass # Description of Blocking Variables 

1 Surname NYSIIS, first name & middle name 

2 City, street NYSIIS, house number & surname NYSIIS 

3 10-digit telephone number 

4 Surname NYSIIS, first name, middle name & city 

5 Surname NYSIIS, first name & city 

6 Surname NYSIIS & city 

7 City, house number & street NYSIIS 

8 City & PO Box 

9 NYSIIS of operation name surname & city 

10 Specific keyword on operation name (oopr_name) 

11 Operation name 

12 First word on the operation name & city 

13 Operation name & city 

14 Zip Code 

15 NYSIIS of surname, first name, NYSIIS of operation surname, operation first name 

16 Surname NYSIIS 

17 Specific key word on the operation name & city 

18 Operation name 

19 7-digit portion of telephone number & 7-digit portion of operation‟s telephone number 

20 Surname NYSIIS, house number & street NYSIIS 

21 Surname NYSIIS & PO box 

22 Surname NYSIIS & house number 
Appendix C 

Table C1: Totals by Model for MT Before Removing SSNs from List Frame File 

Model 

Standard (9-digit) 

4-digit Option 1 

4-digit Option 2 

No SSN/EIN 

Definite 

Matched 

Link 

Groups 

5,595 

5,596 

5,272 

5,259 

FSA Records 

Definite 

Matches to List 

Frame Records 

7,606 

7,606 

7,230 

7,198 

Possible 

Matched 

Link 

Groups 

25,864 

25,992 

26,167 

26,344 

FSA Records 

Possible 

Matches to List 

Frame Records 

114,529 

113,684 

112,429 

111,801 

FSA Records 

Not Matching to 

List Frame 

Records/Link 

5 Groups 

137,223 

138,068 

139,699 

140,359 

Totals FSA 

Records 

259,358 

259,358 

259,358 

259,358 

Table C2: Totals by Model for MT After Removing SSNs from List Frame File 

FSA Records Not Definite FSA Records Possible FSA Records 

Matched Definite Matches Matched Possible Matching to List Totals Model Frame Link to List Frame Link Matches to List FSA 

Groups Records Groups Frame Records Records/Link 

5 Groups Records 

Standard (9-digit) 4,434 5,236 26,965 112,697 141,425 259,358 

4-digit Option 1 4,581 5,398 26,398 112,365 141,595 259,358 

4-digit Option 2 4,088 4,797 26,740 111,221 143,340 259,358 

No SSN/EIN 4,039 4,730 27,406 109,339 145,259 259,358 

Table C3: Totals by Model for SC Before Removing SSNs from List Frame File 

FSA Records Not Definite FSA Records Possible FSA Records 

Matched Definite Matched Possible Matching to List Totals FSA Model Frame Link Matches to List Link Matches to List Records 

Groups Frame Records Groups Frame Records Records/Link 

5 Groups 

Standard (9-digit) 12,040 13,851 25,481 71,856 121,387 207,094 

4-digit Option 1 11,090 12,759 26,190 73,020 121,315 207,094 

4-digit Option 2 11,338 13,048 25,758 71,319 122,727 207,094 

No SSN/EIN 11,085 12,763 26,035 71,264 123,067 207,094 

Table C4: Totals by Model for SC After Removing SSNs from List Frame File 

FSA Records Not Definite FSA Records Possible FSA Records Matching to List Matched Definite Matches Matched Possible Totals Model Frame Link to List Frame Link Matches to List FSA Records/Link Groups Records Groups Frame Records Groups5 Records 

Standard (9-digit) 11,152 12,571 26,658 75,119 119,404 207,094 

4-digit Option 1 10,830 12,302 26,411 76,013 118,779 207,094 

4-digit Option 2 10,764 12,106 26,353 75,117 119,871 207,094 

No SSN/EIN 10,604 11,936 26,847 74,271 120,887 207,094 
Appendix D 

Number of Records Processed by Pass During 2009 FTI Review to Set Cutoffs with 9-digit SSN/EIN, 4-digit 

SSN/EIN with Placename and without SSN/EIN – TX 

# pairs with 

# pairs # pairs with 4-digit Difference Difference Difference 

PASS DESCRIPTION w/out 9-digit SSN/EIN & 9-digit vs 4-digit vs 9-digit vs 

SSN/EIN SSN/EIN placename No Digit No Digit 4-Digit 

Phone (phn) 2,040 563 585 1,477 1,455 -22

Operation (Op) phn and phn 279 118 125 161 154 -7 

Phone and phone other 340 156 163 184 177 -7 

Surname, street, house #, and city 30,081 7,114 7,042 22,967 23,039 72

Opsur/psurname, street, house #, & city 2,639 1,486 1,481 1153 1158 5 

Pfirst, street, house #, and city 25,701 3,692 3,624 22,009 22,077 68

Ofirst/psurname, street, house #, and city 295 185 185 110 110 0 

Pfirst/psurname, street, house #, and city 141 76 76 65 65 0 

Psurname/opsur, street, house #, and city 8,351 3,214 3,200 5,137 5,151 14

Op surname, street, house #, and city 1,485 793 792 692 693 1 

Ppartfirst/pfirst, street, house #, and city 2,395 2,008 2,006 387 389 2 

Op surname/pfirst, street, house #, and city 220 142 142 78 78 0 

surname, box #, and city 12,874 3,501 3,465 9,373 9,409 36

Op/psurname, box #, and city 1,761 973 970 788 791 3 

psurname/opsurname, box #, and city 4,914 1,829 1,809 3,085 3,105 20

nysiis of surname, first, mid, sfx, and city 14,848 1,785 1,739 13,063 13,109 46

wholename and city 8,385 2,437 2,416 5,948 5,969 21

opername and city 4,106 1,057 1,041 3,049 3,065 16

wholename/opername and city 673 233 233 440 440 0 

Opername/ wholename and city 47 21 21 26 26 0 

last, house #, city 32,099 7,579 7,506 24,520 24,593 73

Street, house # and city 32,824 8,655 8,574 24,169 24,250 81

house # and city 34,442 9,178 9,096 25,264 25,346 82

Surname, Street, house # 30,294 7,177 7,146 23,117 23,148 31

Pfirst, house #, city 27,404 3991 3,922 23,413 23,482 69

box # and city 15,874 5,092 5,042 10,782 10,832 50

surname, first, middle, sfx 20,658 2,380 2,374 18,278 18,284 6 

nysiis of surname, first, sfx, and city 39,578 5,979 5,872 33,599 33,706 107

House # 224 70 68 154 156 2 

Pfirst/ofirst, psur/osur city 4,355 1,498 1,487 2,857 2,868 11

oopername and city 480 240 237 240 243 3 

nysiis of surname and city 50,846 13,840 13,722 37,006 37,124 118

op surname /surname and city 5,387 3,170 3,162 2,217 2,225 8 

Popkey/oopkey, city 4,927 1,652 1,639 3,275 3,288 13

o/p first, o/p mid, o/p sur, o/p suf, cty 1,117 667 667 450 450 0 

Totals 422,084 102,551 101,629 319,533 320,455 922