Developing and Evaluating 

Alternative Editing Strategies in the 

Survey of Income and Program 

Participation (SIPP)

Michael D. King

Lindsay M. Monte

Adrianne R. Brown

U.S. Census Bureau

This presentation is released to inform interested parties of ongoing research and to encourage discussion of work in progress. Any 

views expressed are those of the authors and not those of the U.S. Census Bureau. The U.S. Census Bureau has reviewed this data 

product for unauthorized disclosure of confidential information and approved the disclosure avoidance practices applied to this release. 

DRB #CBDRB-FY23-0353.
Large changes in enrollment midyear in 

SNAP and Unemployment Insurance (UI) ...

SNAP Recipients UI Recipients

2

USDA

DOL

Source: U.S. Department of Agriculture (USDA), Food and Nutrition Service; U.S. Department of Labor (DOL), Education and Training 

Administration.
... but SIPP data do not show the expected 

patterns

SNAP Recipients UI Recipients

3

DOL

SIPP

USDA

SIPP

Source: U.S. Census Bureau, 2021 Survey of Income and Program Participation public-use file; U.S. Department of Agriculture (USDA), 

Food and Nutrition Service; U.S. Department of Labor (DOL), Education and Training Administration.
These challenges extend to benefit amounts

• Massive, nationwide investments in these programs resulted in 

substantial increases in benefits during the pandemic:

• As much as $2,400/month additional UI benefits in some months.

• SNAP recipient benefits were maxed out.

• ... but again, the 2021 SIPP data do not always show benefit amounts 

we would expect given these changes.

4
So what could be done? 

• Different challenges for SNAP and UI receipt.

• For SNAP, we believed reports were unexpectedly high for SIPP prior 

to the pandemic.

• For UI, we believed reports were unexpectedly low after the onset of 

the pandemic.

5
So what could be done? (part 2)

SNAP Recipients UI Recipients

6

USDA

SIPP

DOL

SIPP

Source: U.S. Census Bureau, 2021 Survey of Income and Program Participation public-use file; U.S. Department of Agriculture (USDA), 

Food and Nutrition Service; U.S. Department of Labor (DOL), Education and Training Administration.
So what could be done? (part 3)

• Rely on known policies to help bring amounts closer to expectations.

• For SNAP, known maximum allotment based on number of people in 

SNAP unit.

• For UI, known minimum and maximum benefits and wage 

replacement rates for each state.

7
What did we actually do?

• Generated new variable values in a traditional edit process.

• Worked within production framework for coding these new edits.

• Used released production data as inputs.

• No modeling and no administrative records.

8
Changes to 

survey

Develop 

instrument

Interview 

households

Transcode 

& reformat

Edit

& weight

Create 

public-use 

data

Publish 

data

Publish 

research

Changes to 

survey

Develop 

instrument

Interview 

households

Transcode 

& reformat

Edit

& weight

Create 

public-use 

data

Publish 

data

Publish 

research

9

research file

SIPP lifecycle
What did we actually do? (part 2)

• SNAP

• Looked for inconsistencies between SNAP reports from prior year 

and current year.

• Used unit size and maximum allotment information from Food and 

Nutrition Service (FNS) to set amounts .

• UI

• Identified cases that experienced job separations longer than 1 

week and assigned UI in months that job separation continued.

• Used available wage data from pre-pandemic and state policies to 

estimate UI amounts.

10
What did we actually do? (part 3)

11

Persons

Changed

Person-Months 

Changed

Receipt

SNAP 426 1,533

UI 2,394 28,179

Amounts

SNAP 3,459 28,890

UI 3,714 31,615

Source: U.S. Census Bureau, 2021 Survey of Income and Program Participation research file (DRB #CBDRB-FY23-0353).
How well did the alternative edits do?

SNAP Recipients UI Recipients

12

DOL SIPP research file

USDA

SIPP research file

SIPP public-use file

SIPP public-use file

Source: U.S. Census Bureau, 2021 Survey of Income and Program Participation public-use file and 2021 Survey of Income and Program 

Participation research file (DRB #CBDRB-FY23-0353); U.S. Department of Agriculture (USDA), Food and Nutrition Service; U.S. 

Department of Labor (DOL), Education and Training Administration.
How well did the alternative edits do? (part 2)

• SNAP data matched to limited number of SNAP state administrative 

records.

• UI data matched to 1099-G administrative records.

• Mixed results from these comparisons.

13
What did we learn?

• Working within production environment came with constraints.

• New edits more complex than expected.

• Did get closer to aggregate benchmarks and patterns.

• Accuracy of individual changes difficult to evaluate.

• Some core assumptions flawed.

14
What’s next?

• Falling response rates and rising imputation rates remain universal 

challenges.

• Logical, edit-based imputations have limits.

• Further explore model-based options.

• Consider question wording.

15
Thank you

Mike King

michael.king2@census.gov

Lindsay Monte

lindsay.m.monte@census.gov

Adri Brown

adrianne.r.brown@census.gov

16
2019 2020 2021

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6

2020 SIPP Reference Year 2020 SIPP Int. Year

2021 SIPP Reference Year 2021 SIPP Int. Year

2022 SIPP Ref. Year

How do we do it? – SNAP

17

• Look for inconsistencies between SNAP reports from prior year and 

current year.
2019 2020 2021

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6

2020 SIPP Reference Year 2020 SIPP Int. Year

2021 SIPP Reference Year 2021 SIPP Int. Year

2022 SIPP Ref. Year

How do we do it? – SNAP

18

• Look for inconsistencies between SNAP reports from prior year and 

current year.