On Implementing a New 

Imputation Method into 

Production in the 2017 Economic 

Census Illustrated through 

Selected Vignettes

Katherine Jenny Thompson and Laura Bechtel

Economic Statistical Methods Division

The views expressed in this presentation are those of the authors and not necessarily 

those of the U.S. Census Bureau
General Set Up

BIG Problem 

Research Team Solution Implementation Team 

Production

New

2
The Next Three Presentations

BIG 

Problem 

Research 

Team 

Implementation Team 

New 

Method(s)

Production

New

3
The Next Three Presentations

BIG 

Problem 

Research 

Team 

Production

New

4

Storage

Existing 

Processes Additional 

Requirements

Integration 

challenges

Implementation Team
My Discussion

Methods for treating 

missing product data

Research Team Solution Implementation Team 

Production

New

5
Major Changes for the 

2017 Economic Census

 North American Product Classification (NAPCS) - nearly 

3,000 broad products and 5,000 detail products

 Missing data treatment problem

 New data item(s) problem

 New economy-wide product-by-industry table problem

 All electronic data collection

 Only one option for reporting product sales data –

rounded to $1,000

6
Example of Product Questions

 Retail Trade Industry – New Car Dealers

7
8

Detail 

Products

Broad product subtotal
9
Multiple 

blank 

spaces to

write-in 

products 

not listed

Notes:

• Respondents caミ さ┘rite-iミざ products that are ミot 

pre-listed

• All detail product values should sum to their 

associated broad product value

• All broad product values should sum to the 

reported total value of sales (within a tolerance) 

for the establishment

10
Missing Data Treatment

Research Team

•  1,000 industries

•  8,000 products

• Broad products

• Detail products

• Sample data (i.e., sampling 

weights)

• Imputation cells

• Cell collapsing rules 

• Minimum number of donors

• Restrictions on value ( > $1,000)

11
Research Challenges

 Team Composition (next slide)

 Short time frame ( 6-9 months)

 Magnitude of the problem

  1,000 industries and  8,000 products

 Historical data limitations (new collection)

12
Research Team Composition

 Methodologists

 Very limited experience with Economic Census

 E┗eミ less experieミce ┘ith さproductsざ

 Subject matter experts

 Very limited experience with imputation 

methodology

 NO Programmers/IT Specialists

13
Missing Data Treatment

Research Team

•  1,000 industries

•  8,000 products

• Broad products

• Detail products

• Sample data (i.e., sampling 

weights)

• Imputation cells

• Cell collapsing rules 

• Minimum number of donors

• Restrictions on value ( > $1,000)

14
Missing Data Treatment

Research Team

• 1,000 25 industries

• 8,000 Top 10 products

• Broad products

• Detail products

• Sample data (i.e., sampling 

weights)

• LOCAL Imputation cells

• Cell collapsing rules 

• Minimum number of donors

• Restrictions on value ( > $1,000)

15
Quick Summary

 Research conducted under restricted conditions 

 SAS code developed in-house 

 Not ready for prime time!

 Recommendation: Hot Deck

 Nearest neighbor or random

 Unaddressed production requirements

16
Implementation Team

17

Research

Team

Implementation 

Team

Common members (overlap)

• Overlap

• Team leader: Subject matter expert 

• Consultants: Methodologists (4) 

• New members

• Subject Matter Experts

• Programmers

• Methodologists (1 new)
Implementation Team

18

Research

Team

Implementation 

Team

Common members (overlap)
Implementation Team

Topic Team Members Knowledge

Level

Economic Census Processing Subject Matter Expert

Methodologists Varied

Programmers/IT Expert

Hot Deck Imputation Subject Matter Low

Methodologists Expert

Programmers/IT Low

19
Missing Data Treatment

Research Team

• 1,000 25 industries

• 8,000 Top 10 products

• Broad products

• Detail products

• Sample data (i.e., sampling 

weights)

• LOCAL Imputation cells

• Cell collapsing rules 

• Minimum number of donors

• Restrictions on value ( > $1,000)

Implementation Team

•  1,000 industries

•  8,000 products

• Broad products

• Detail products

• Sample data (i.e., sampling 

weights)

• Imputation cells 

• Cell collapsing rules

• Minimum number of donors

• Restrictions on value ( > $1,000)

20
Missing Data Treatment

Research Team

• 1,000 25 industries

• 8,000 Top 10 products

• Broad products

• Detail products

• Sample data (i.e., sampling 

weights)

• LOCAL Imputation cells

• Cell collapsing rules 

• Minimum number of donors

• Restrictions on value ( > $1,000)

Implementation Team

•  1,000 industries

•  8,000 products

• Broad products

• Detail products

• Sample data (i.e., sampling 

weights)

• Imputation cells 

• Cell collapsing rules

• Minimum number of donors

• Restrictions on value ( > $1,000)

• さMustざ products for iミdustries

• Choice of hot deck method by 

industry

• Maximizing use of reported data

• Backup methods

• Processing time 

21
Missing Data Treatment

Research Team

• 1,000 25 industries

• 8,000 Top 10 products

• Broad products

• Detail products

• Sample data (i.e., sampling 

weights)

• LOCAL Imputation cells

• Cell collapsing rules 

• Minimum number of donors

• Restrictions on value ( > $1,000)

Implementation Team

•  1,000 industries

•  8,000 products

• Broad products

• Detail products

• Sample data (i.e., sampling 

weights)

• Imputation cells 

• Cell collapsing rules

• Minimum number of donors

• Restrictions on value ( > $1,000)

• さMustざ products for iミdustries

• Choice of hot deck method by 

industry

• Maximizing use of reported data

• Backup methods

• Processing time 

22
Vignettes

1. Processing time 

2. Imputation Cell Collapsing/Minimal Donor

3. Imputation for Detail Products

23
Vignette 1: Processing Time

 Programmer concerns

 Prohibitive processing time

 Complex coding

 Addressed by

 Testing methods

 Test deck of 2.4 million donor records (with over 20 

million products) and 1.1 million full recipients

 Independent validation of production code

 Collaborative development of specifications

24
Vignette 2: Imputation Cells

 Needed

 Imputation cell definitions

 Collapsing rules

 Minimum donors

 Limited research

 Education Process

25
Simple Example

 Cell Collapsing

 Ideal = Industry By Tax Status By Unit Type

 Collapse 1 = Industry By Tax Status 

 DROP Unit Type

 Base = Industry

 DROP Unit Type and Tax Status

 Minimum cell count = 5

 Base = 1

26
Compute Donor Counts 

Ideal Cells

Industry Tax Exempt Status Unit Type # of Establishments

AAAAAA Taxable SU 2

Taxable MU 2

Exempt SU 2

Exempt MU 2

BBBBBB Taxable SU 23

Taxable MU 35

Exempt SU 2

Exempt MU 4

CCCCCC Taxable SU 10

Taxable MU 3

Exempt SU 200

Exempt MU 2

27
Determine Usage of Ideal Cells

Industry Tax Exempt Status Unit Type # of Establishments

AAAAAA Taxable SU 2

Taxable MU 2

Exempt SU 2

Exempt MU 2

BBBBBB Taxable SU 23

Taxable MU 35

Exempt SU 2

Exempt MU 4

CCCCCC Taxable SU 10

Taxable MU 3

Exempt SU 200

Exempt MU 2

Less than 5 

Establishments 

Collapse

28
Compute Donor Counts for 

Collapse 1 Cells

Industry Tax Exempt Status # of Establishments

AAAAAA Taxable 4

Exempt 4

BBBBBB Taxable 58

Exempt 6

CCCCCC Taxable 13

Exempt 202

STILL Less than 5 

Establishments 

Collapse

All Counts  5 

Establishments 

Use Collapse 1 cells

29
Compute Donor Counts 

for Base Cells

Industry # of Establishments

AAAAAA 8

BBBBBB 58

CCCCCC 10

All Counts  1

Establishments 

Use base cells

30
Final Cells for Hot Deck

Industry Tax Exempt Status Unit Type Hot Deck Cell

AAAAAA ALL ALL Base

BBBBBB Taxable SU Ideal

Taxable MU Ideal

Exempt ALL Collapse 1

CCCCCC Taxable ALL Collapse 1

Exempt ALL Collapse 1

31
Final Cells for Hot Deck

Industry Tax Exempt Status Unit Type Hot Deck Cell

AAAAAA ALL ALL Base

BBBBBB Taxable SU Ideal

Taxable MU Ideal

Exempt ALL Collapse 1

CCCCCC Taxable ALL Collapse 1

Exempt ALL Collapse 1

32
The Contention Point

Industry Tax Exempt Status Unit Type # of Establishments

CCCCCC Taxable SU 10

Taxable MU 3

Exempt SU 200

Exempt MU 2

33

Subject Matter Experts and Programmers Contention

• NO need to collapse for SU unit type 

• さNOT FAIRざ 
Alternate Proposal

Industry Tax Exempt 

Status

Unit Type Imputation Cell # of 

Establishments

CCCCCC Taxable SU Ideal 10

Taxable MU Collapse 1 13

Exempt SU Ideal 200

Exempt MU Collapse 1 202

34

Methodology Concerns (Severe)

• Imputation cells no longer disjoint 

• Affects variance estimation 

• Hurts probability of selecting MU donors 
Impasse?

35

FOR ALTERNATIVE

Subject matter experts

Programmers/IT specialists

Methodologists (2.5)

AGAINST ALTERNATIVE

Methodologists (2.5)
Compromise/Decision

 Alternative Proposal

 Minimum number of donors = 1 

 Lessons learned:

 Put in measures to avoid unacceptable compromises

 Include programmers and subject matter experts at 

the research stage 

36
Vignette 3: Detail Products

 Not considered during research stage

 Limited historic data for research

 Businesses more likely to report broad products 

than detail products

 Different types of details by industry

 Subject matter experts wanted to maximize 

use of valid reported data in imputation

37
Imputation of Detail Products

 Group establishments into types

 Use category average (ratio) imputation 

 Statistical model frequently used for business data

 NOT part of the research for product data

 さHot deckざ iマputatioミ – all products (broad & 

detail)

38
Establishment Groups

Donors Broad products usable

Complete All required Detail products balance to Broad products

Partial Some usable Detail products

Minimal No usable Detail products

Recipients Missing products 

Full Need Broad products and Detail products

Partial Need some (designated) Detail products

Minimal Need all designated Detail products

Ineligible All produIts usaHle, Hut ミot さt┞piIalざ; e┝Iluded froマ doミor 

pool

39
Complete Donor Example

Sales BP 1 DP11 DP12DP13

BP 2 DP21

DP22

BP 3 DP31

DP32 DP33

BP 4

40
Partial Donor Example

Sales BP 1 DP11 DP12DP13

BP 2

BP 3 DP31

DP32 DP33

BP 4

41

No usable detail products

for broad product 2
Mimimal Donor Example

Sales BL 1

BL 2

BL 3

BL 4

42

No usable detail products

for any broad product
“Completing” Partial Donors

Sales BL 1 DP11 DP12 DP13

BL 2

BL 3

BL 4

Missing detailed products for BL 2 and 3

I

n

p

u

t

s

Category Averages for BL 2 and 3 

BL 2 DP21

85%

DP22

10%

BL 3 DP31

99%

DP32

0.7%

DP33

0.3%

DP23

4.5%

DP24

0.5%

O

u

t

p

u

t

Sales BL 1 DP11 DP12 DP13

BL 2

BL 3

BL 4

$236

DP21 DP22

DP31 DP32

DP23 DP24

Completed 

record is now a 

donor

43

Average proportion that each detail 

product contributes to a broad product
Establishment Groups

Donors Broad products usable

Complete All required Detail products add to Broad products

Partial Some usable Detail products

Minimal No usable Detail products

Recipients Missing products 

Full Need Broad products and Detail products

Partial Need some (designated) Detail products

Minimal Need all designated Detail products

Ineligible All produIts usaHle, Hut ミot さt┞piIalざ; e┝Iluded froマ doミor 

pool

44

These units are both

donors and recipients!
Going Back to Original Picture

BIG Problem 

Research Team Solution Implementation Team 

Production

New

45
Summary

ADVANTAGES

DISADVANTAGES
Summary

ADVANTAGES

DISADVANTAGES

• Workable solutions

• Buy-in on methods

• Shared understanding

• Research ideas for 2022

• A fe┘ さless thaミ perfectざ 

decisions

• Many extra meetings
Thank you

Contacts: 

Katherine.J.Thompson@census.gov

Laura.Bechtel@census.gov

48